Multi-Scale Contextual Coding for Human-Machine Vision of Volumetric Medical Images by Jietao Chen, Weijie Chen, Qianjian Xing, Feng Yu

“…Experimental results demonstrate that our framework obtains an average 9% BD-Rate reduction over the Versatile Video Coding (VVC) anchor on MRNet datasets, while achieving superior recognition performance for downstream segmentation and classification tasks than inputting reconstructed lossy images.…”
Get full text

Adaptive weighted dual MAML: Proposing a novel method for the automated diagnosis of partial sleep deprivation. by Soraya Khanmohmmadi, Toktam Khatibi, Golnaz Tajeddin, Elham Akhondzadeh, Amir Shojaee

“…Our method, Adaptive Weighted Dual MAML, combines two base models-a ResNet and a CNN-Transformer-within the MAML framework, which leverages multi-shot tasks to improve the EEG signal classification.…”
Get full text

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. …”
Get full text

Federated Deep Learning for Scalable and Explainable Load Forecasting in Privacy-Conscious Smart Cities by Ibrahim Alzamil

“…To address these issues, this study proposes HHCTE-FL, a Hierarchical Hybrid Convolutional Transformer Extractor embedded within a federated learning framework. …”
Get full text

Plant Disease Prognosis Using Spatial-Exploitation-Based Deep-Learning Models by Jayavani Vankara, Sekharamahanti S. Nandini, Murali Krishna Muddada, N. Satya Chitra Kuppili, K Sowjanya Naidu

“…In this paper, we present a framework for automating disease detection by the use of a tailored DL architecture. …”
Get full text

Research on freeze-thaw displacement prediction model of sandy soil based on attention mechanism CNN-BiGRU by Zecheng Wang, Dongwei Li, Zhengbin Dong, Zhiwen Jia, Chaochao Zhang

“…This study develops an attention-based CNN-BiGRU model that synergizes convolutional neural networks for spatial feature extraction, bidirectional gated recurrent units for temporal dependency modeling, and attention mechanisms for critical time-step weighting. …”
Get full text

Classification of Mycena and <i>Marasmius</i> Species Using Deep Learning Models: An Ecological and Taxonomic Approach by Fatih Ekinci, Guney Ugurlu, Giray Sercan Ozcan, Koray Acici, Tunc Asuroglu, Eda Kumru, Mehmet Serdar Guzel, Ilgaz Akata

“…In this study, we developed a novel deep learning-based framework for the classification of seven macrofungi species from the genera <i>Mycena</i> and <i>Marasmius</i>, leveraging their unique ecological and morphological characteristics. …”
Get full text

Sparse Temporal Data-Driven SSA-CNN-LSTM-Based Fault Prediction of Electromechanical Equipment in Rail Transit Stations by Jing Xiong, Youchao Sun, Junzhou Sun, Yongbing Wan, Gang Yu

“…To solve the problem of predicting faults in station mechanical and electrical equipment with sparse data, this study proposes a fault prediction framework based on SSA-CNN-LSTM. Firstly, this article proposes a fault enhancement method for station electromechanical equipment based on TimeGAN, which expands and generates data that conform to the temporal characteristics of the original dataset, to solve the problem of sparse data in the original fault dataset. …”
Get full text

FL-TENB4: A Federated-Learning-Enhanced Tiny EfficientNetB4-Lite Approach for Deepfake Detection in CCTV Environments by Jimin Ha, Abir El Azzaoui, Jong Hyuk Park

“…This paper proposes FL-TENB4, a Federated-Learning-enhanced Tiny EfficientNetB4-Lite framework for deepfake detection in CCTV environments. …”
Get full text

GCAFlow: Multi-Scale Flow-Based Model with Global Context-Aware Channel Attention for Industrial Anomaly Detection by Lin Liao, Congde Lu, Yujie Gao, Hao Yu, Biao Cai

“…In addition, we design a hierarchical convolutional subnetwork to improve the probabilistic modeling capacity of the flow-based framework. …”
Get full text

Self-Supervised Keypoint Learning for the Geometric Analysis of Road-Marking Templates by Chayanon Sub-r-pa, Rung-Ching Chen

“…To address this, we propose GeoTemplateKPNet, a novel self-supervised deep-learning framework, built upon Convolutional Neural Networks (CNNs), designed to learn robust, geometrically consistent keypoints specifically in synthetic template images. …”
Get full text

Intelligent waste sorting for urban sustainability using deep learning by Gulzar Ahmad, Fizza Muhammad Aleem, Tahir Alyas, Qaiser Abbas, Waqas Nawaz, Taher M. Ghazal, Abdul Aziz, Saira Aleem, Nadia Tabassum, Aidarus Mohamed Ibrahim

“…In this paper, we present an intelligent waste classification system that utilises Convolutional Neural Networks (CNNs) for automatic segregation into twelve categories of waste, employing image data. …”
Get full text

A nnU-Net-based automatic segmentation of FCD type II lesions in 3D FLAIR MRI images by Shubham Joshi, Millie Pant, Millie Pant, Arnav Malhotra, Kusum Deep, Vaclav Snasel

“…The nnU-Net framework is known for its ability to adapt its settings, including preprocessing, network design, training, and post-processing, to any new medical imaging task. …”
Get full text

Enhanced Curvature-Based Fabric Defect Detection: A Experimental Study with Gabor Transform and Deep Learning by Mehmet Erdogan, Mustafa Dogan

“…Furthermore, we implemented and evaluated several other methods from the literature, including Gabor and Convolutional Neural Networks (CNNs), within a unified coding framework. …”
Get full text

Character generation and visual quality enhancement in animated films using deep learning by Weiran Cao, Zhongbin Huang

“…This work aims to optimize the first order motion model (FOMM) to enhance its performance in generating animated character images. To this end, the convolutional block attention module (CBAM) is introduced into FOMM. …”
Get full text

Unsupervised Deep Clustering on Spatiotemporal Objects Extracted from 4D Point Clouds for Automatic Identification of Topographic Processes in Natural Environments by J. Wang, K. Anders

“…In this paper, we present a time series-based unsupervised deep clustering framework for identifying topographic processes without manual feature engineering and annotations. …”
Get full text

Rolling bearing fault diagnosis under small sample conditions based on WDCNN-BiLSTM Siamese network by Chenxu Bian, Chunni Jia, Jibo Li, Xiangjun Chen, Pei Wang

“…Additionally, the SNN framework is introduced to build a feature space under small sample conditions through metric learning, enhancing the ability of model to discern sample similarities. …”
Get full text

PoulTrans: a transformer-based model for accurate poultry condition assessment by Jun Li, Bing Yang, Junyang Chen, Jiaxin Liu, Felix Kwame Amevor, Guanyu Chen, Buyuan Zhang, Xiaoling Zhao

“…To address this, we present PoulTrans, an innovative image captioning framework that leverages a Convolutional Neural Network (CNN) integrated with a CSA_Encoder-Transformer architecture to generate detailed poultry status reports. …”
Get full text

Advancing AI Interpretability in Medical Imaging: A Comparative Analysis of Pixel-Level Interpretability and Grad-CAM Models by Mohammad Ennab, Hamid Mcheick

“…This study introduces the Pixel-Level Interpretability (PLI) model, a novel framework designed to address critical limitations in medical imaging diagnostics by enhancing model transparency and diagnostic accuracy. …”
Get full text

HEE-SegGAN: A holistically-nested edge enhanced GAN for pulmonary nodule segmentation. by Yong Wang, Seri Mastura Mustaza, Mohammad Syuhaimi Ab-Rahman, Siti Salasiah Mokri

“…In this study, we proposed HEE-SegGAN, a holistically-nested edge-enhanced generative adversarial networks, which integrated HED-U-Net with a GAN framework to improve model robustness and edge segmentation accuracy. …”
Get full text