Ground-Target Recognition Method Based on Transfer Learning by Qiuzhan Zhou, Jikang Hu, Huinan Wu, Cong Wang, Pingping Liu, Xinyi Yao

“…We proposed a new moving ground-target recognition algorithm based on CNN and domain adaptation. We used convolutional neural networks (CNNS) to extract depth features from target vibration signals to identify target types. …”
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Deep Learning for Predicting Spheroid Viability: Novel Convolutional Neural Network Model for Automating Quality Control for Three-Dimensional Bioprinting by Zyva A. Sheikh, Oliver Clarke, Amatullah Mir, Narutoshi Hibino

“…In this study, we build a convolutional neural network (CNN) model to efficiently and accurately predict spheroid viability, using a phase-contrast image of a spheroid as its input. …”
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Real-Time Quality Monitoring and Anomaly Detection for Vision Sensors in Connected and Autonomous Vehicles by Elena Politi, Charalampos Davalas, Christos Chronis, George Dimitrakopoulos, Dimitrios Michail, Iraklis Varlamis

“…On this basis we adopt a two-stage approach to validate the performance of the proposed methods against a baseline Convolutional Neural Network (CNN) in a controlled low-criticality environment, as well as in more complex real-world scenarios. …”
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QuantumNet: An enhanced diabetic retinopathy detection model using classical deep learning-quantum transfer learning by Manish Bali, Ved Prakash Mishra, Anuradha Yenkikar, Diptee Chikmurge

“…The method is as follows: • Evaluate three classical deep learning models—CNN, ResNet50, and MobileNetV2—using the APTOS 2019 blindness detection dataset on Kaggle to identify the best-performing model for integration. • QuantumNet combines the best-performing classical DL model for feature extraction with a variational quantum classifier, leveraging quantum transfer learning for enhanced diagnostics, validated statistically and on Google Cirq using standard metrics. • QuantumNet achieves 94.11 % accuracy, surpassing classical DL models and prior research by 11.93 percentage points, demonstrating its potential for accurate, efficient DR detection and broader medical imaging applications.…”
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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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Spam detection using hybrid model on fusion of spammer behavior and linguistics features by Amna Iqbal, Muhammad Younas, Saman Iftikhar, Fakeeha Fatima, Rabia Saleem

“…Various deep learning approaches have been proposed for review spamming, including different neural networks (Convolutional Neural Network, CNN). These methods are specialized in extracting the features but lack to capture feature dependencies effectively with other features. …”
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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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Animal Species Identification in Historical Parchments by Continuous Wavelet Transform–Convolutional Neural Network Classifier Applied to Ultraviolet–Visible–Near-Infrared Spectros... by Nicolas Roy, Henry Pièrard, Julie Bouhy, Alexandre Mayer, Olivier Deparis, David Gravis

“…The network architecture chosen was CWT-CNN (continuous wavelet transform–convolutional neural network), which, in this case, is composed of a convolutional autoencoder and a single-layer dense network classifier. …”
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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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A human-on-the-loop approach for labelling seismic recordings from landslide site via a multi-class deep-learning based classification model by Jiaxin Jiang, David Murray, Vladimir Stankovic, Lina Stankovic, Clement Hibert, Stella Pytharouli, Jean-Philippe Malet

“…Leveraging on recent recommendations on embedding humans in the Artificial Intelligence (AI) decision making process, particularly training and validation, we propose a methodology that incorporates data labelling, verification, and re-labelling through a multi-class convolutional neural network (CNN) supported by Explainable Artificial Intelligence (XAI) tools, specifically, Layer-wise Relevance Propagation (LRP). …”
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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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Applying Hybrid Deep Learning Models to Assess Upper Limb Rehabilitation by Sheng Miao, Zitong Liu, Dezhen Wang, Xiang Shen, Nana Shen

“…The method uses a monocular camera to capture video data from the patient’s upper limb rehabilitation training, utilizes Faster R-CNN and HRNet to recognize the human body position and upper limb bone key point information, and then builds a long short-term memory (LSTM) neural network model incorporating the ProbSparse Self-Attention mechanism to evaluate the rehabilitation training movements. …”
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A hybrid framework for colorectal cancer detection and U-Net segmentation using polynetDWTCADx by Akella S Narasimha Raju, K Venkatesh, Makineedi Rajababu, Ranjith Kumar Gatla, Marwa M. Eid, Enas Ali, Nataliia Titova, Ahmed B. Abou Sharaf

“…The study employs DWT to optimize and enhance two integrated CNN models before classifying them with SVM following a systematic procedure. …”
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