Deep Learning-Based Denoising for Optical Coherence Tomography: Evaluating Self-Supervised and Generative Models Across Retinal Datasets by Diogen BABUC, Alesia LOBONŢ, Alexandru FARCAŞ, Todor IVAŞCU, Sebastian-Aurelian ŞTEFĂNIGĂ

“…We used OCT scans with different retinal diseases datasets, such as diabetic retinopathy, age-related macular degeneration, macular hole, central serous retinopathy, and normal retinas. …”
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Unsupervised Domain Adaptation via Contrastive Learning and Complementary Region-Class Mixing by Xiaojing Li, Wei Zhou, Mingjian Jiang

“…However, these deep models have poor generalization ability across different domain datasets. To alleviate the degradation of the model’s performance in different domains, unsupervised domain adaptation attempts to transfer the knowledge from a labeled source domain to an unlabeled target domain. …”
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Deep Learning Ensemble Approach for Predicting Expected and Confidence Levels of Signal Phase and Timing Information at Actuated Traffic Signals by Seifeldeen Eteifa, Amr Shafik, Hoda Eldardiry, Hesham A. Rakha

“…This architecture is combined with different deep learning methods, including Multilayer Perceptrons (MLP), Long-Short-Term Memory neural networks (LSTM), and Convolutional Long-Short-Term Memory neural networks (CNNLSTM) to form an ensemble of predictors. …”
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GraphGIM: rethinking molecular graph contrastive learning via geometry image modeling by Chaoyi Li, Hongxin Xiang, Wenjie Du, Tengfei Ma, Haowen Chen, Xiangxiang Zeng, Lei Xu

“…Furthermore, we find that as the convolutional layers process the image becomes deeper, the information of feature maps gradually changes from global molecular-level information (molecular scaffolds) to local atomic-level information (molecular atoms and functional groups), which provides chemical information at different scales. …”
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Information-Guided Diffusion Model for Downscaling Land Surface Temperature from SDGSAT-1 Remote Sensing Images by Jianxin Wang, Zhitao Fu, Bohui Tang, Jianhui Xu

“…Additionally, the study reveals the spatial feature impact of different auxiliary information in LST downscaling and the variations in features across different regions and temperature ranges.…”
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Predicting the thermal conductivity of polymer composites with one-dimensional oriented fillers using the combination of deep learning and ensemble learning by Yinzhou Liu, Weidong Zheng, Haoqiang Ai, Lin Cheng, Ruiqiang Guo, Xiaohan Song

“…However, for composites consist of different matrices and fillers, the CNN model needs to be retrained, which greatly wastes computing resources. …”
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Interpretable multi-instance heterogeneous graph network learning modelling CircRNA-drug sensitivity association prediction by Mengting Niu, Chunyu Wang, Yaojia Chen, Quan Zou, Ximei Luo

“…Abstract Background Different expression levels of circular RNAs (circRNAs) affect the sensitivity of human cells to drugs, thus producing different responses to the therapeutic effects of drugs. …”
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SET: A Shared-Encoder Transformer Scheme for Multi-Sensor, Multi-Class Fault Classification in Industrial IoT by Kamran Sattar Awaisi, Qiang Ye, Srinivas Sampalli

“…Our experimental results indicate that SET consistently outperforms baseline methods, including Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN)-LSTM, and Multilayer Perceptron (MLP), as well as the proposed comparative variant of SET, Multi-Encoder Transformer (MET), in terms of accuracy, precision, recall, and F1-score across different fault intensities. …”
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The Impact of Integrating Shallow and Deep Information on Knowledge Distillation by Yilin Miao, Yuhong Tang, Huangliang Ren, Jianjun Li

“…To address these issues, we propose a shallow feature extraction module (SFEM) that enriches the receptive field of images through dilated convolutions and captures information at different scales, while reducing parameter counts and optimizing computational resources. …”
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Impact of eye fundus image preprocessing on key objects segmentation for glaucoma identification by Sandra Virbukaitė, Jolita Bernatavičienė

“…The variety in images caused by different eye fundus cameras makes the complexity for the existing deep learning (DL) networks in OD and OC segmentation. …”
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Using the antibody-antigen binding interface to train image-based deep neural networks for antibody-epitope classification. by Daniel R Ripoll, Sidhartha Chaudhury, Anders Wallqvist

“…We evaluated this approach using Ab sequences derived from human HIV and Ebola viral infections to differentiate between two Abs, Abs belonging to specific B-cell family lineages, and Abs with different epitope preferences. In addition, we explored a different type of DNN method to detect one class of Abs from a larger pool of Abs. …”
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Research on mechanical automatic food packaging defect detection model based on improved YOLOv5 algorithm. by Guanyong Liu, Shuai Zhang, Lixin Wang, Xiaoran Li, Gongchen Li

“…Secondly, feature fusion across scales is achieved with pyramid and aggregation networks, so that the model can capture defects of different sizes at the same time, which enhances the recognition ability of diverse defects in food packaging. …”
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Automatic Road Extraction from Historical Maps Using Transformer-Based SegFormers by Elif Sertel, Can Michael Hucko, Mustafa Erdem Kabadayı

“…In this research, we aim to automatically extract five different road types from historical maps, using a road dataset digitized from the scanned Deutsche Heereskarte 1:200,000 Türkei (DHK 200 Turkey) maps. …”
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Cross-Architecture Vulnerability Detection Combining Semantic and Attribute Feature by LI Kun, LI Bin, ZHU Wenjing, ZHOU Qinglei

“…The twin network model based on convolutional neural network is used to generate function-level embedding vectors, in order to extract the features of different spatial hierarchies in different basic blocks and reduce the number of parameters in the neural network. …”
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A Deep Learning-Based Diagnostic Framework for Shaft Earthing Brush Faults in Large Turbine Generators by Katudi Oupa Mailula, Akshay Kumar Saha

“…A key innovation lies in the use of FFT-derived spectrograms from both voltage and current waveforms as dual-channel inputs to the CNN, enabling automatic feature extraction of time–frequency patterns associated with different SEB fault types. The proposed framework combines advanced signal processing and convolutional neural networks (CNNs) to automatically recognize fault-related patterns in shaft grounding current and voltage signals. …”
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IoTShield: Defending IoT Systems Against Prevalent Attacks Using Programmable Networks by Mah-Rukh Fida, Azza H. Ahmed, Ameer Shakayb Arsalaan

“…Furthermore, a single DDoS attacks detector based on lightweight Decision Tree (DT) model in the data plane, achieves 80-99% of accuracy in detecting different types of attack flows, with fine-grained classification offloaded to the control plane where a Convolutional Neural Network (CNN) classifier achieves 99% accuracy. …”
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Topic Words-Based Multilingual Hateful Linguistic Resources Construction for Developing Multilingual Hateful Content Detection Model Using Deep Learning Technique by Naol Bakala Defersha, Kula Kekeba Tune, Solomon Teferra Abate

“…Nowadays, social media platforms provide space that allows communication and sharing of various resources using a variety of natural languages in different cultural and multilingual aspects. Although this interconnectedness offers numerous benefits, it also exposes users to the risk of encountering offensive (OFFN) and harmful content, including hateful speech. …”
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Plant leaf classification using the multiscale entropy of curvature and feature aggregation by Raphael G. Pinheiro, José G.F. Lopes, Marcelo M.S. Souza, Fátima N.S. Medeiros

“…The results also confirm that the proposed strategy outperformed six different sets of deep features according to the F1-score and accuracy. …”
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Federated learning applications in soil spectroscopy by Giannis Gallios, Nikolaos Tsakiridis, Nikolaos Tziolas

“…Each scenario was investigated under two different averaging aggregation strategies: Federated Averaging (FedAvg) and Weighted Averaging (WgtAvg), which are used to develop a consensus model by aggregating the weights of the different contributors. …”
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MSLKSTNet: Multi-Scale Large Kernel Spatiotemporal Prediction Neural Network for Air Temperature Prediction by Feng Gao, Jiaen Fei, Yuankang Ye, Chang Liu

“…The core module of this network, Multi-scale Spatiotemporal Attention (MSSTA), decomposes large kernel convolutions from multi-scale perspectives, capturing spatial feature information at different scales, and focuses on the evolution of multi-scale spatial features over time, encompassing both global smooth changes and local abrupt changes. …”
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