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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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

“…Firstly, we integrated a Convolutional Attention module (CBAM) to enhance the model's attention on crucial image features. …”
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Automatic Road Extraction from Historical Maps Using Transformer-Based SegFormers by Elif Sertel, Can Michael Hucko, Mustafa Erdem Kabadayı

“…There is a growing need to explore transformer-based approaches for geospatial object extraction from historical maps, given their superior performance over traditional convolutional neural network (CNN)-based architectures. …”
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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

“…This paper presents a methodology for classifying plant leaves on the basis of handcrafted features derived from the multiscale entropy of curvature and texture, as well as deep features obtained from convolutional neural networks (CNNs). We propose three object descriptors on the basis of the multiscale entropy of curvature. …”
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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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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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Integrating Viewing Direction and Image Features for Robust Multi-View Multi-Object 3D Pedestrian Tracking by R. Ali, M. Mehltretter, C. Heipke

“…However, many of these developments do not address the critical challenge of generalization across different camera constellations, i.e., having camera constellations that differ between training and testing, limiting their effectiveness in real-world applications. …”
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A High-Precision Defect Detection Approach Based on BiFDRep-YOLOv8n for Small Target Defects in Photovoltaic Modules by Yi Lu, Chunsong Du, Xu Li, Shaowei Liang, Qian Zhang, Zhenghui Zhao

“…Secondly, for the multi-scale characteristics of defects, the neck network is optimized by introducing a bidirectional weighted feature pyramid network (BiFPN), which adopts an adaptive weight allocation strategy to enhance feature fusion and improve the characterization of defects at different scales. Finally, the detection head part uses DyHead-DCNv3, which combines the triple attention mechanism of scale, space, and task awareness, and introduces deformable convolution (DCNv3) to improve the modeling capability and detection accuracy of irregular defects.…”
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SDMSEAF-YOLOv8: a framework to significantly improve the detection performance of unmanned aerial vehicle images by Linxuan Li, Xiaoyu Liu, Xuan Chen, Fengjuan Yin, Bin Chen, Yufeng Wang, Fanbin Meng

“…A Space-to-depth layer replaces the traditional strided convolution layer to enhance the extraction of fine-grained information and small-sized target features. …”
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Fine-Grained Extraction of Coastal Aquaculture Ponds From Remote Sensing Images Using an Edge-Supervised Multi-task Neural Network by Jian Qi, Min Ji, Fengxiang Jin, Jianran Xu, Hanyu Ji, Juan Wang

“…Furthermore, transfer experiments with JL1 imagery from Jiangmen and Yantai demonstrate the strong generalization capability of the proposed method across different environments.…”
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Real-Time Waste Detection and Classification Using YOLOv12-Based Deep Learning Model by Mosharof Hossain Dipo, Fahmid Al Farid, Md. Sifti Al Mahmud, Muntasir Momtaz, Shakila Rahman, Jia Uddin, Hezerul Abdul Karim

“…Images of waste were captured in many different settings and analyzed with a YOLOv12-based model. …”
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A Hybrid Framework for Photovoltaic Power Forecasting Using Shifted Windows Transformer-Based Spatiotemporal Feature Extraction by Ping Tang, Ying Su, Weisheng Zhao, Qian Wang, Lianglin Zou, Jifeng Song

“…The experimental results indicate that the proposed framework demonstrates competitive predictive performance and generalization capability across different time horizons and weather conditions compared with benchmark frameworks.…”
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