Effective data selection via deep learning processes and corresponding learning strategies in ultrasound image classification by Hyunju Lee, Jin Young Kwak, Eunjung Lee

“…Additionally, the True network showed strong performance when applied to the Vision Transformer and similar enhancements were observed across multiple convolutional neural network architectures. Furthermore, to assess the robustness and adaptability of our method across different medical imaging modalities, we applied it to dermoscopic images and observed similar performance enhancements. …”
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Img2Neuro: brain-trained neural activity encoders for enhanced object recognition by Mona A Aboelnaga, Mohamed W El-Kharashi, Seif Eldawlatly

“…Therefore, rather than using the brain as an inspiration, in this paper, we introduce Img2Neuro; a convolutional neural network model feature extractor that predicts the visual brain’s response to images by encoding neural activity. …”
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Improving drug-induced liver injury prediction using graph neural networks with augmented graph features from molecular optimisation by Taeyeub Lee, Joram M. Posma

“…Methods We evaluated several GNN architectures, including Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), Graph Sample and Aggregation (GraphSAGE), and Graph Isomorphism Networks (GINs), using the latest FDA DILI dataset and other molecular property prediction datasets. …”
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Contextual Deep Semantic Feature Driven Multi-Types Network Intrusion Detection System for IoT-Edge Networks by Shaho Hassen, Ahmed Abdlrazaq

“… Recent years have witnessed an exponential rise in wireless networks and allied interoperable distributed computing frameworks, where the different sensory units transfer real-world event data to the network analyzer for run-time decisions. …”
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Edge-based detection and localization of adversarial oscillatory load attacks orchestrated by compromised EV charging stations by Khaled Sarieddine, Mohammad Ali Sayed, Sadegh Torabi, Ribal Atallah, Chadi Assi

“…The results demonstrate the effectiveness of the implemented approach with the Convolutional Long-Short Term Memory model producing optimal classification accuracy (99.4%). …”
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Sugarcane leaf disease classification using deep neural network approach by Saravanan Srinivasan, S. M. Prabin, Sandeep Kumar Mathivanan, Hariharan Rajadurai, Suresh Kulandaivelu, Mohd Asif Shah

“…Methods In order to identify the diseases in sugarcane leaves, this study used EfficientNet architectures along with other well-known convolutional neural network (ConvNet) models such as DenseNet201, ResNetV2, InceptionV4, MobileNetV3 and RegNetX. …”
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Research on Bearing Fault Diagnosis Method for Varying Operating Conditions Based on Spatiotemporal Feature Fusion by Jin Wang, Yan Wang, Junhui Yu, Qingping Li, Hailin Wang, Xinzhi Zhou

“…Experimental results demonstrate that STFDAN achieves high diagnostic accuracy across different load conditions and effectively solves the bearing fault diagnosis problem under varying operating conditions.…”
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Personalized region of interest recommendation through adaptive fusion of multi-dimensional user preferences by Qing Tang, Shenghua Xu, Zhuolu Wang, Yong Wang, Shichuan Liu, Haiqin Hua

“…Finally, an adaptive weighting model is introduced to integrate the spatio-temporal, social, and category preferences, assigning individual preference weights to different users to facilitate personalized ROI recommendation. …”
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Wireless Channel Prediction Using Artificial Intelligence With Imperfect Datasets by Gowhar Javanmardi, Ramiro Samano Robles

“…The effectiveness of prediction is severely degraded when sets are undersampled or subject to low SNR. Convolutional NN (CNN) and particularly LSTM (Long Short-Term Memory) show more resilience to these impairments. …”
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A Model for Diagnosing Mild Nutrient Stress in Facility-Grown Tomatoes Throughout the Entire Growth Cycle by Yunpeng Yuan, Guoxiang Sun, Guangyu Chen, Qihua Zhang, Lingwei Liang

“…This study proposes a deep learning framework based on CNN + LSTM, using canopy near-infrared spectroscopy from different growth stages of tomatoes as input, to diagnose mild stress of nitrogen (N), potassium (K), and calcium (Ca) throughout the entire growth cycle of facility-grown tomatoes. …”
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Comprehensive Quantitative Analysis of Coal-Based Liquids by Mask R-CNN-Assisted Two-Dimensional Gas Chromatography by Huan-Huan Fan, Xiang-Ling Wang, Jie Feng, Wen-Ying Li

“…Regional labels associated with areas in the GC × GC chromatograms were fed into the mask-region-based convolutional neural network’s (Mask R-CNN’s) algorithm. …”
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An enhanced lightweight model for apple leaf disease detection in complex orchard environments by Ge Wang, Wenjie Sang, Fangqian Xu, Yuteng Gao, Yue Han, Qiang Liu

“…Additionally, we design a detail-enhanced shared convolutional scaling detection head (DESCS-DH) to enable the model to effectively capture edge information of diseases and address issues such as poor performance in object detection across different scales. …”
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Assessing Revisit Risk in Emergency Department Patients: Machine Learning Approach by Wang-Chuan Juang, Zheng-Xun Cai, Chia-Mei Chen, Zhi-Hong You

“…Furthermore, this study evaluates different ML models, feature sets, and feature encoding methods in order to build an effective prediction model. …”
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Deep Learning Technology for Weld Defects Classification Based on Transfer Learning and Activation Features by Chiraz Ajmi, Juan Zapata, Sabra Elferchichi, Abderrahmen Zaafouri, Kaouther Laabidi

“…The main objective of this work is to explore the capacity of AlexNet and different pretrained architecture with transfer learning for the classification of X-ray images. …”
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An Efficient Semantic Segmentation Framework with Attention-Driven Context Enhancement and Dynamic Fusion for Autonomous Driving by Jia Tian, Peizeng Xin, Xinlu Bai, Zhiguo Xiao, Nianfeng Li

“…The encoder leverages a high-efficiency backbone, while the decoder introduces a dynamic fusion mechanism designed to enhance information interaction between different feature branches. Recognizing the limitations of convolutional networks in modeling long-range dependencies and capturing global semantic context, the model incorporates an attention-based feature extraction component. …”
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MCGFE-CR: Cloud Removal With Multiscale Context-Guided Feature Enhancement Network by Qiang Bie, Xiaojie Su

“…Currently, cloud removal methods with better performance are mainly based on Convolutional Neural Networks (CNNs). However, they fail to capture global context information, resulting in the loss of global context features in image reconstruction. …”
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Artificial intelligence based classification and prediction of medical imaging using a novel framework of inverted and self-attention deep neural network architecture by Junaid Aftab, Muhammad Attique Khan, Sobia Arshad, Shams ur Rehman, Dina Abdulaziz AlHammadi, Yunyoung Nam

“…After that, two novel custom deep learning architectures were introduced, called the Inverted Residual Convolutional Neural Network (IRCNN) and Self Attention CNN (SACNN). …”
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MDFT-GAN: A Multi-Domain Feature Transformer GAN for Bearing Fault Diagnosis Under Limited and Imbalanced Data Conditions by Chenxi Guo, Vyacheslav V. Potekhin, Peng Li, Elena A. Kovalchuk, Jing Lian

“…To improve classification performance, an Enhanced Hybrid Visual Transformer (EH-ViT) is constructed by coupling a lightweight convolutional stem with a ViT encoder, enabling robust and discriminative fault identification. …”
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Thyroid nodule segmentation in ultrasound images using transformer models with masked autoencoder pre-training by Yi Xiang, Rajendra Acharya, Quan Le, Jen Hong Tan, Chiaw-Ling Chng

“…Unlike traditional convolutional neural networks (CNNs), transformers capture global context from the first layer, enabling more comprehensive image representation, which is crucial for identifying subtle nodule boundaries. …”
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SIG-ShapeFormer: A Multi-Scale Spatiotemporal Feature Fusion Network for Satellite Cloud Image Classification by Xuan Liu, Zhenyu Lu, Bingjian Lu, Zhuang Li, Zhongfeng Chen, Yongjie Ma

“…SIG-Shapeformer consists of three core components: (1) a Shapelet-based module that captures discriminative and interpretable local temporal patterns; (2) a multi-scale Inception module combining 1D convolutions and Transformer encoders to extract temporal features across different scales; and (3) a differentially enhanced Gramian Angular Summation Field (GASF) module that converts time series into 2D texture representations, significantly improving the recognition of cloud internal structures. …”
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