Innovative Framework for Historical Architectural Recognition in China: Integrating Swin Transformer and Global Channel–Spatial Attention Mechanism by Jiade Wu, Yang Ying, Yigao Tan, Zhuliang Liu

“…Through extensive experiments on a constructed historical building dataset, our model achieves an outstanding performance of over 97.8% in key metrics including accuracy, precision, recall, and F1 score (harmonic mean of the precision and recall), surpassing traditional CNN (convolutional neural network) architectures and contemporary deep learning models. …”
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Transformer-Based Optimization for Text-to-Gloss in Low-Resource Neural Machine Translation by Younes Ouargani, Noussaim El Khattabi

“…With a 55.18 Recall-Oriented Understudy for Gisting Evaluation (ROUGE) score, and a 63.6 BiLingual Evaluation Understudy 1 (BLEU1) score, our proposed model not only outperforms state-of-the-art models on the Phoenix14T dataset but also outperforms some of the best alternative architectures, specifically Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU). …”
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YOLOv8n-CA: Improved YOLOv8n Model for Tomato Fruit Recognition at Different Stages of Ripeness by Xin Gao, Jieyuan Ding, Ruihong Zhang, Xiaobo Xi

“…When compared to seven other models—Faster R-CNN, YOLOv3s, YOLOv5s, YOLOv5m, YOLOv7, YOLOv8n, YOLOv10s, and YOLOv11n—the YOLOv8n-CA model was the smallest in size and demonstrated superior detection performance.…”
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Edge-AI Enabled Wearable Device for Non-Invasive Type 1 Diabetes Detection Using ECG Signals by Maria Gragnaniello, Vincenzo Romano Marrazzo, Alessandro Borghese, Luca Maresca, Giovanni Breglio, Michele Riccio

“…A spectrogram-based preprocessing method is combined with a 1-Dimensional Convolutional Neural Network (1D-CNN) to analyze the ECG signals directly on the device. …”
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Diagnosis and Detection of Alzheimer’s Disease Using Learning Algorithm by Gargi Pant Shukla, Santosh Kumar, Saroj Kumar Pandey, Rohit Agarwal, Neeraj Varshney, Ankit Kumar

“…After pre-processing, we proposed three learning algorithms for AD classification, that is random forest, XGBoost, and Convolution Neural Networks (CNN). Results are computed on dataset and show that it outperformed with exiting work in terms of accuracy is 97.57% and sensitivity is 97.60%.…”
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Squeeze-and-Excitation Vision Transformer for Lung Nodule Classification by Xiaozhong Xue, Yanhe Ma, Weiwei Du, Yahui Peng

“…This result shows a significant improvement compared to ViT and SE-CNN.…”
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Research on Mental Health Monitoring Scheme of Migrant Children Based on Convolutional Neural Network Based on Deep Learning by Guangyan Yang

“…The main research focus is feature extraction, trying to use one-dimensional convolutional neural network (1D-CNN) to mine students’ online patterns from online behavior sequences, calculate abnormal scores based on students’ consumption data in the cafeteria, and describe the dietary differences among students. …”
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Deep learning for enhanced risk management: a novel approach to analyzing financial reports by Xiangting Shi, Yakang Zhang, Manning Yu, Lihao Zhang

“…This research brings out the Hybrid Financial Risk Predictor (HFRP) model, using the convolutional neural networks (CNN) and long-short term memory (LSTM) networks to improve financial risk prediction. …”
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A Data-Driven Fault Prediction Method for Nuclear Power Systems Based on End-to-End Deep Learning Framework by Lu Chao, Chunbing Wang, Shuai Chen, Qizhi Duan, Hongyun Xie

“…For this purpose, we present an end-to-end deep network for nuclear power system prediction (EDN-NPSP), which can automatically mine the transient features of various detection data in the NPS at the current moment through heterogeneous convolution kernels that can increase the receptive field and then predict the feature evolution results of the NPS in the future through a special deep CNN. The results provide an assessment of the future state of NPS. …”
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Securing Brain-to-Brain Communication Channels Using Adversarial Training on SSVEP EEG by Hossein Ahmadi, Ali Kuhestani, Mohammadreza Keshavarzi, Luca Mesin

“…We utilized a combined Convolutional Neural Network-Temporal Convolutional Network (CNN-TCN) architecture to classify the data and assessed the system’s resistance to various adversarial strategies, including Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), Basic Iterative Method (BIM), Carlini & Wagner (C&W), and Momentum Iterative Method (MIM). …”
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Introducing an ensemble method for the early detection of Alzheimer's disease through the analysis of PET scan images by Arezoo Borji, Taha-Hossein Hejazi, Abbas Seifi

“…In this paper, three deep-learning models, namely VGG16 and AlexNet, and a custom Convolutional Neural Network (CNN) with 8-fold cross-validation, have been used for classification. …”
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Onboard Processing of Hyperspectral Imagery: Deep Learning Advancements, Methodologies, Challenges, and Emerging Trends by Nafiseh Ghasemi, Jon Alvarez Justo, Marco Celesti, Laurent Despoisse, Jens Nieke

“…This article discusses the efficacy of different network architectures, highlighting the advantages of lightweight CNN models and 1D-CNNs for onboard processing. Moreover, the potential of hardware accelerators, particularly field programmable gate arrays, for enhancing processing efficiency is explored. …”
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Interpretable DWT-1DCNN-LSTM Network for Power Quality Disturbance Classification by Shuangquan Yang, Tao Shan, Xiaomei Yang

“…Experimental validation with simulated datasets demonstrates that the DWT-1DCNN-LSTM model achieves an accuracy of 99.27%, outperforming the DWT-1DCNN, 1DCNN-LSTM, LSTM, and CNN models by 1.59%, 1.13%, 1.44%, and 6.48%, respectively. …”
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Design, Multiperspective Investigations, and Performance Analysis of Multirotor Unmanned Aerial Vehicle for Precision Farming by Darshan Kumar Jayaram, Vijayanandh Raja, Beena Stanislaus Arputharaj, Hussein A. Z. AL-bonsrulah

“…Three AI systems were tested on different datasets to forecast plant stress by analyzing leaves due to technical constraints. CNN’s accuracy and computing speed make it ideal for precision farming. …”
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The Use of Machine Learning to Support the Diagnosis of Oral Alterations by Rosana Leal do Prado, Juliane Avansini Marsicano, Amanda Keren Frois, Jacques Duílio Brancher

“…Material and Methods: The study compares three convolutional neural network (CNN) architectures for classifying histological images: EfficientNet-B3, MobileNet-V2, and VGG16. …”
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Synthetic Network and Search Filter Algorithm in English Oral Duplicate Correction Map by Xiaojun Chen

“…On the basis of word vectors, the advantages and disadvantages of CNN, LSTM, and SVM models in this shared task are analyzed through experimental data. …”
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A deep learning approach for early prediction of breast cancer neoadjuvant chemotherapy response on multistage bimodal ultrasound images by Jiang Xie, Jinzhu Wei, Huachan Shi, Zhe Lin, Jinsong Lu, Xueqing Zhang, Caifeng Wan

“…The code will be published on the https://github.com/jinzhuwei/BLTA-CNN .…”
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Combined Oriented Data Augmentation Method for Brain MRI Images by Ahmeed Suliman Farhan, Muhammad Khalid, Umar Manzoor

“…The proposed method helps CNN models overcome overfitting and address class imbalance issues by combining Brain MRI images to generate new images. …”
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A semi-supervised deep neuro-fuzzy iterative learning system for automatic segmentation of hippocampus brain MRI by M Nisha, T Kannan, K Sivasankari

“…Unlike the existing approaches such as UNet and Convolutional Neural Networks (CNN), the proposed algorithm generates an image that is similar to a real image by learning the distribution much more quickly by the semi-supervised iterative learning algorithm of the Deep Neuro-Fuzzy (DNF) technique. …”
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Identification of Civil Infrastructure Damage Using Ensemble Transfer Learning Model by A. Shamila Ebenezer, S. Deepa Kanmani, V. Sheela, K. Ramalakshmi, V. Chandran, M. G. Sumithra, B. Elakkiya, Bharani Murugesan

“…In this paper, an ensemble of three CNN models is proposed, and two are transfer learning-based models. …”
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