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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MEViT: Generalization of Deepfake Detection With Meta-Learning EfficientNet Vision Transformer by Van-Nhan Tran, Hoanh-Su Le, Piljoo Choi, Suk-Hwan Lee, Ki-Ryong Kwon

“…With the rapid advances in deep generative models, the accessibility and sophistication of such manipulation technologies are increasing, making it more challenging to detect fake content. Different facial forgery techniques result in complex data distributions, and most existing deepfake detection approaches rely on convolutional neural networks (CNNs) that treat the task as a binary classification problem. …”
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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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Ensemble Deep Learning Object Detection Fusion for Cell Tracking, Mitosis, and Lineage by Imad Eddine Toubal, Noor Al-Shakarji, D. D. W. Cornelison, Kannappan Palaniappan

“…Building upon our previous work, we propose a new deep learning-based method, EDNet, for cell detection, tracking, and motility analysis that is more robust to shape across different cell lines, and models cell lineage and proliferation. …”
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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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TADNet: A Time and Attention-Based Point Cloud Denoising Network for Autonomous Driving in Adverse Weather by Y. Zhang, H. Huang, X. Yan, Y. Liang, Y. Li, J. Yang

“…The experimental results show that the denoising effect of TADNet in three kinds of bad weather, namely rain, snow and fog, is better than other methods, which can remove different kinds of noise with different intensities and retain the environmental features, and has the best performance of IoU and MIoU in all kinds of weather conditions.…”
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GLAI-Net: Global–Local Awareness Integrated Network for Semantic Change Detection in Remote Sensing Images by Qing Ding, Fengyan Wang, Mingchang Wang, Ying Zhang, Gui Cheng

“…Meanwhile, we propose multi-scale feature fusion (MSFF) modules in GLAI-Net to enhance the focus of detail features on changed objects with different sizes. Between the classification and change detection decoding branches, we propose semantic change response (SCR) modules in GLAI-Net that fully utilize the correlation between different tasks to improve the consistency and accuracy of detection results. …”
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Fault Diagnosis Based On Improved Information Entropy And 1dcnn For Marine Turbocharger Rotor With Variable Speed by Hu Lei, Hu Haoran, Hu Nao, Liu Luyuan, Dong Fei, Yang Jianguo, Zhong Jiahong

“…Faults in the turbocharger rotor at the different speeds are classified using a one-dimensional convolutional neural network (1DCNN), and the arithmetic ability of the diagnostic algorithm is evaluated. …”
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Segmented Curve-Fitting Method for Continuum Removal in CRISM MTRDR data by P. Kumari, S. Soor, A. Shetty, S. G. Koolagudi

“…The identification score is improved by around 8% for the similarity matching method Weighted Sum of Spectrum Correlation and by around 1.5% for a Convolutional Neural Network. Furthermore, an SCF-based mineral identification framework demonstrates its effectiveness in identifying the dominant minerals on CRISM MTRDR hyperspectral data collected from different locations on the Martian surface.…”
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Employing sentinel-2 time-series and noisy data quality control enhance crop classification in arid environments: A comparison of machine learning and deep learning methods by Zahra Mohammadi Mobarakeh, Saeid Pourmanafi, Mohsen Ahmadi

“…In this study, we employed a novel hybrid approach, integrating time-series analysis, noisy data quality control, and different machine learning and deep learning models to classify croplands of complex multi-crop systems in central Iran. …”
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Soil moisture retrieval over agricultural region through machine learning and sentinel 1 observations by Deepanshu Lakra, Deepanshu Lakra, Shobhit Pipil, Prashant K. Srivastava, Suraj Kumar Singh, Manika Gupta, Rajendra Prasad

“…The backscattering coefficients were taken as the input variables and SM as the output variable for the training and testing of different models. The performance analysis of RMSE, R-squared, and correlation coefficients revealed that the Random Forest (RF) and Convolutional Neural Network (CNN) models demonstrated superior performance for SM estimation over the wheat field. …”
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Hearing vocals to recognize schizophrenia: speech discriminant analysis with fusion of emotions and features based on deep learning by Jie Huang, Yanli Zhao, Zhanxiao Tian, Wei Qu, Xia Du, Jie Zhang, Meng Zhang, Yunlong Tan, Zhiren Wang, Shuping Tan

“…Current diagnostic criteria rely primarily on clinical symptoms, which may not fully capture individual differences and the heterogeneity of the disorder. In this study, a discriminative model of schizophrenic speech based on deep learning is developed, which combines different emotional stimuli and features. …”
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First-Arrival Picking for Microseismic Monitoring Based on Deep Learning by Xiaolong Guo

“…In microseismic monitoring, achieving an accurate and efficient first-arrival picking is crucial for improving the accuracy and efficiency of microseismic time-difference source location. In the era of big data, the traditional first-arrival picking method cannot meet the real-time processing requirements of microseismic monitoring process. …”
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