High-Level Interpretation of Urban Road Maps Fusing Deep Learning-Based Pixelwise Scene Segmentation and Digital Navigation Maps by Carlos Fernández, Jesús Muñoz-Bulnes, David Fernández-Llorca, Ignacio Parra, Iván García-Daza, Rubén Izquierdo, Miguel Á. Sotelo

“…On the other hand, the sensing module provides a pixelwise segmentation of the road using a ResNet-101 CNN with random data augmentation, as well as other hand-crafted features such as curbs, road markings, and vegetation. …”
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Advanced Text Summarization Model Incorporating NLP Techniques and Feature-Based Scoring by Estabraq Abdulreda Kadhim, Mohammad-Reza Feizi-Derakhshi, Hadi S. Aghdasi

“…This study’s summarization algorithm was tested using the CNN, XSum and BBC Summarization datasets, which aggregate documents from different areas. …”
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Testing convolutional neural network based deep learning systems: a statistical metamorphic approach by Faqeer ur Rehman, Clemente Izurieta

“…We further use mutation testing techniques to show the usefulness of the proposed approach in the healthcare space and test two CNN-based deep learning models (used for pneumonia detection among patients). …”
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Potential for Vertical Heterogeneity Prediction in Reservoir Basing on Machine Learning Methods by Hongqing Song, Shuyi Du, Ruifei Wang, Jiulong Wang, Yuhe Wang, Chenji Wei, Qipeng Liu

“…Moreover, the overall AARD of the predictive result obtained by the CNN method was controlled at 11.51%, revealing the highest accuracy compared with BP and LSTM neural networks. …”
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ViT-DualAtt: An efficient pornographic image classification method based on Vision Transformer with dual attention by Zengyu Cai, Liusen Xu, Jianwei Zhang, Yuan Feng, Liang Zhu, Fangmei Liu

“…In this paper, we propose a novel pornographic image classification model named ViT-DualAtt. The model adopts a CNN-Transformer hierarchical structure, combining the strengths of Convolutional Neural Networks (CNNs) and Transformers to effectively capture and integrate both local and global features, thereby enhancing feature representation accuracy and diversity. …”
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Automated karyogram analysis for early detection of genetic and neurodegenerative disorders: a hybrid machine learning approach by Sumaira Tabassum, M. Jawad Khan, Javaid Iqbal, Asim Waris, M. Adeel Ijaz

“…It is fine-tuned on labeled data, followed by a classification step using a Convolutional Neural Network (CNN). A unique dataset of 234,259 chromosome images, including the training, validation, and test sets, was used. …”
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An Unconventional Approach for Analyzing the Mechanical Properties of Natural Fiber Composite Using Convolutional Neural Network by Govindaraj Ramkumar, Satyajeet Sahoo, G. Anitha, S. Ramesh, P. Nirmala, M. Tamilselvi, Ram Subbiah, S. Rajkumar

“…The developed convolutional neural network (CNN) is used to accurately predict the mechanical properties of these composites. …”
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Enhanced Intrusion Detection in Drone Networks: A Cross-Layer Convolutional Attention Approach for Drone-to-Drone and Drone-to-Base Station Communications by Mohammad Aldossary, Ibrahim Alzamil, Jaber Almutairi

“…The proposed technique is shown to be necessary and effective by real-world drone communication dataset evaluations. CLCAN outperforms CNN, LSTM, and XGBoost with 98.4% accuracy, 98.7% recall, and 98.1% F1-score. …”
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Large-scale high uniform optoelectronic synapses array for artificial visual neural network by Fanqing Zhang, Chunyang Li, Zhicheng Chen, Haiqiu Tan, Zhongyi Li, Chengzhai Lv, Shuai Xiao, Lining Wu, Jing Zhao

“…Finally, the established artificial visual convolutional neural network (CNN) through optical/electrical signal modulation can reach the high digit recognition accuracy of 96.5%. …”
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Monitoring Soil Salinity in Arid Areas of Northern Xinjiang Using Multi-Source Satellite Data: A Trusted Deep Learning Framework by Mengli Zhang, Xianglong Fan, Pan Gao, Li Guo, Xuanrong Huang, Xiuwen Gao, Jinpeng Pang, Fei Tan

“…These variables are then integrated into various machine learning models—such as Ensemble Tree (ETree), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and LightBoost—as well as deep learning models, including Convolutional Neural Networks (CNN), Residual Networks (ResNet), Multilayer Perceptrons (MLP), and Kolmogorov–Arnold Networks (KAN), for modeling. …”
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Multi-Temporal Energy Management Strategy for Fuel Cell Ships Considering Power Source Lifespan Decay Synergy by Xingwei Zhou, Xiangguo Yang, Mengni Zhou, Lin Liu, Song Niu, Chaobin Zhou, Yufan Wang

“…The study designs an attention-based CNN-LSTM hybrid model for power prediction and constructs a two-stage optimization framework: The first stage employs Model Predictive Control (MPC) for long-term power planning to optimize equivalent hydrogen consumption, while the second stage focuses on real-time power allocation considering both power source degradation and system operational efficiency. …”
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A Hybrid Approach for Sports Activity Recognition Using Key Body Descriptors and Hybrid Deep Learning Classifier by Muhammad Tayyab, Sulaiman Abdullah Alateyah, Mohammed Alnusayri, Mohammed Alatiyyah, Dina Abdulaziz AlHammadi, Ahmad Jalal, Hui Liu

“…The system utilized a hybrid CNN (Convolutional Neural Network) + RNN (Recurrent Neural Network) classifier for event recognition, with Grey Wolf Optimization (GWO) for feature selection. …”
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BMNet: Enhancing Deepfake Detection Through BiLSTM and Multi-Head Self-Attention Mechanism by Demao Xiong, Zhan Wen, Cheng Zhang, Dehao Ren, Wenzao Li

“…When forgery techniques can generate highly realistic videos, traditional convolutional neural network (CNN)-based detection models often struggle to capture subtle forgery features and temporal dependencies. …”
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A Novel Audio Copy Move Forgery Detection Method With Classification of Graph-Based Representations by Beste Ustubioglu, Gul Tahaoglu, Arda Ustubioglu, Guzin Ulutas, Muhammed Kilic

“…Graph coloring algorithms are applied to convert the graph into a visual representation, which is then input into a specially designed Convolutional Neural Network (CNN) model for classification. The trained model was evaluated using five different datasets, demonstrating that this approach generally outperforms existing methods in terms of detection accuracy. …”
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Normalized difference vegetation index prediction using reservoir computing and pretrained language models by John Olamofe, Ram Ray, Xishuang Dong, Lijun Qian

“…Using MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m SIN Grid V061 dataset, we designed and implemented Reservoir Computing (RC) models and transformer-based models including pretrained language model, and compared the prediction performance of these models to traditional machine learning and deep learning methods such as Nonlinear Regression, Decision Tree, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and DLinear. …”
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A Synchronized Hybrid Brain-Computer Interface System for Simultaneous Detection and Classification of Fusion EEG Signals by Dalin Yang, Trung-Hau Nguyen, Wan-Young Chung

“…Furthermore, a four-layer convolutional neural network (CNN) is used as a classifier to distinguish different mental tasks. …”
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Multi-Scale Building Load Forecasting Without Relying on Weather Forecast Data: A Temporal Convolutional Network, Long Short-Term Memory Network, and Self-Attention Mechanism Appro... by Lanqian Yang, Jinmin Guo, Huili Tian, Min Liu, Chang Huang, Yang Cai

“…The experimental results show that on multiple time scales, the TCN–LSTM–self-attention prediction model proposed in this paper is more accurate than the LSTM, CNN-LSTM, and TCN-LSTM models. Especially in the task of predicting cooling, heating, and electrical loads on a 1-week scale, the model proposed in this paper achieves improvements of 16.58%, 6.77%, and 3.87%, respectively, in the RMSE indicator compared with the TCN-LSTM model.…”
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DGFEG: Dynamic Gate Fusion and Edge Graph Perception Network for Remote Sensing Change Detection by Shengning Zhou, Genji Yuan, Zhen Hua, Jinjiang Li

“…Numerous networks combining CNN and transformer architectures have emerged, yet effectively balancing local detail and global context features remains a topic of ongoing discussion. …”
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Automated String Art Creation: Integrated Advanced Computational Techniques and Precision Art Designing by Spoorthi Singh, Navya T. Hegde, Mohammad Zuber, Yuvraj Singh Nain, Vishnu G. Nair

“…A convolutional neural network (CNN) was employed to process grayscale images, extracting and reconstructing features using pooling and deconvolution techniques, with the model achieving stable performance over multiple epochs. …”
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Breast mass classification based on supervised contrastive learning and multi‐view consistency penalty on mammography by Lilei Sun, Jie Wen, Junqian Wang, Zheng Zhang, Yong Zhao, Guiying Zhang, Yong Xu

“…In this paper, A novel classification algorithm based on Convolutional Neural Network (CNN) is proposed to improve the diagnostic performance for breast cancer on mammography. …”
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