An Efficient and Hybrid Deep Learning-Driven Model to Enhance Security and Performance of Healthcare Internet of Things by Muhammad Babar, Muhammad Usman Tariq, Basit Qureshi, Zabeeh Ullah, Fahim Arif, Zahid Khan

“…It then makes an informed decision about whether to send the data to the fog layer. The CNN approach is also included in the suggested framework to choose the best fog node. …”
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IoT based intelligent pest management system for precision agriculture by Salman Ahmed, Safdar Nawaz Khan Marwat, Ghassen Ben Brahim, Waseem Ullah Khan, Shahid Khan, Ala Al-Fuqaha, Slawomir Koziel

“…The size of the dataset is 1000+ images categorized into two groups: (1) fruit fly and (2) not fruit fly and a convolutional neural network (CNN) classifier was trained based on the following features: (1) Haralick features (2) Histogram of oriented gradients (3) Hu moments and (4) Color histogram. …”
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Explainable AI-Based Approach for Age-Related Macular Degeneration (AMD) Detection via Fundus Imaging by Ainhoa Osa-Sanchez, Hossam Magdy Balaha, Ali Mahmoud, Ashraf Sewelam, Mohammed Ghazal, Begonya Garcia-Zapirain, Ayman El-Baz

“…The Transformer model achieved its highest accuracy of 83.72% (with a sensitivity of 83.86% and a specificity of 89.74%). The CNN model demonstrated the best performance, with an accuracy of 94.19% (with a sensitivity of 93.84% and a specificity of 96.00%). …”
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MCPA: multi-scale cross perceptron attention network for 2D medical image segmentation by Liang Xu, Mingxiao Chen, Yi Cheng, Pengwu Song, Pengfei Shao, Shuwei Shen, Peng Yao, Ronald X. Xu

“…Abstract The UNet architecture, based on convolutional neural networks (CNN), has demonstrated its remarkable performance in medical image analysis. …”
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Stock price prediction with attentive temporal convolution-based generative adversarial network by Ying Liu, Xiaohua Huang, Liwei Xiong, Ruyu Chang, Wenjing Wang, Long Chen

“…This approach employs a GAN framework to generate stock price data using an attentive temporal convolutional network as a generator, whereas a CNN-based discriminator evaluates the authenticity of the data. …”
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Leveraging advanced deep learning and machine learning approaches for snow depth prediction using remote sensing and ground data by Haytam Elyoussfi, Abdelghani Boudhar, Salwa Belaqziz, Mostafa Bousbaa, Karima Nifa, Bouchra Bargam, Abdelghani Chehbouni

“…The models evaluated include two ML approaches: Support Vector Regression (SVR) and eXtreme Gradient Boosting (XGBoost) and four DL models: 1-Dimensional Convolutional Neural Network (1D-CNN), Long Short-Term Memory Networks (LSTM), Gated Recurrent Unit (GRU), and Bi-directional Long Short-Term Memory Network (Bi-LSTM). …”
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Deep learning empowered sensor fusion boosts infant movement classification by Tomas Kulvicius, Dajie Zhang, Luise Poustka, Sven Bölte, Lennart Jahn, Sarah Flügge, Marc Kraft, Markus Zweckstetter, Karin Nielsen-Saines, Florentin Wörgötter, Peter B. Marschik

“…Convolutional neural network (CNN) architectures were used to classify movement patterns. …”
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Rapid Detection of Hybrid Maize Parental Lines Using Stacking Ensemble Machine Learning by M. Aqil, M. Azrai, M. J. Mejaya, N. A. Subekti, F. Tabri, N. N. Andayani, Rahma Wati, S. Panikkai, S. Suwardi, Z. Bunyamin, E. Roy, M. Muslimin, M. Yasin, E. Prakasa

“…Among all the evaluated CNN architecture and stacking models, Inception V3-embedded images with logistic regression metaclassifier outperformed other models with accuracy of about 98%. …”
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Concept of a geoinformation platform for landmines and other explosive objects detection and mapping with UAV by Mykhailo Popov, Sergey Stankevich, Sergey Mosov, Stanislav Dugin, Stanislav Golubov, Artem Andreiev, Artur Lysenko, Ievgen Saprykin

“…Future studies will involve extensive experimental testing and may involve convolutional neural networks (CNN) as a detection mechanism.…”
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Deep-learning-based extraction of circle of Willis topology with anatomical priors by Dieuwertje Alblas, Iris N. Vos, Micha M. Lipplaa, Christoph Brune, Irene C. van der Schaaf, Mireille R. E. Velthuis, Birgitta K. Velthuis, Hugo J. Kuijf, Ynte M. Ruigrok, Jelmer M. Wolterink

“…These fields are obtained using a scale-invariant and rotation-equivariant mesh-CNN-based model (SIRE). For a 3D TOF-MRA volume, a potentially overcomplete graph of the CoW is thus extracted in which each edge represents an artery. …”
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Multi-Feature Driver Variable Fusion Downscaling TROPOMI Solar-Induced Chlorophyll Fluorescence Approach by Jinrui Fan, Xiaoping Lu, Guosheng Cai, Zhengfang Lou, Jing Wen

“…Various machine learning models, including CNN, Stacking, Extreme Random Trees, AdaBoost, and GBDT, were compared, with Random Forest yielding the best performance, achieving R² = 0.931, RMSE = 0.052 mW/m²/nm/sr, and MAE = 0.031 mW/m²/nm/sr for 2018–2019 and R² = 0.926, RMSE = 0.058 mW/m²/nm/sr, and MAE = 0.034 mW/m²/nm/sr for 2019–2020. …”
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The Prevalence of Star-forming Clumps as a Function of Environmental Overdensity in Local Galaxies by Dominic Adams, Hugh Dickinson, Lucy Fortson, Kameswara Mantha, Vihang Mehta, Jürgen Popp, Claudia Scarlata, Chris Lintott, Brooke Simmons, Mike Walmsley

“…To obtain our clump sample, we use a Faster R-CNN object detection network trained on the catalog of clump labels provided by the Galaxy Zoo: Clump Scout project, then apply this network to detect clumps in approximately 240,000 Sloan Digital Sky Survey galaxies (originally selected for Galaxy Zoo 2). …”
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Sparse attention convolution-ViT model for working condition recognition in zinc flotation by Yue SU, Zhaohui TANG, Yongfang XIE, Xiaoliang GAO, Hu ZHANG, Weiye MA, Haiyang TANG

“…The model achieved a recognition accuracy of 88.62% on the zinc flotation froth image dataset, surpassing traditional CNN and ViT models. Ablation experiments highlighted the critical role of the sparse multi-head attention mechanism and the attention-gated unit, contributing to accuracy improvements of 0.92% and 2.63%, respectively. …”
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Continuous Speech-Based Fatigue Detection and Transition State Prediction for Air Traffic Controllers by Susmitha Vekkot, Surya Teja Chavali, Charan Tej Kandavalli, Rama Sai Abhishek Podila, Deepa Gupta, Mohammed Zakariah, Yousef Ajami Alotaibi

“…The evaluation was carried out using various learning algorithms such as XGBoost, Adaboost, Random Forest, HistogramGB, and 1D-CNN. The ensemble algorithms demonstrated the best performance, achieving a maximum accuracy of 100% on the XGBoost test set. …”
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Measuring Fear and Greed Index in Stock Market: Evidence from the Tehran Stock Exchange by Mojtaba Rostami Noroozabad, Ali Golbabaei Pasandi, Milad Shahrazi, Somayeh Esfandyari

“…To apply the Fear and Greed Index in the Tehran Stock Exchange, we adapted CNN's Fear and Greed Index, making some modifications. …”
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MSCPNet: A Multi-Scale Convolutional Pooling Network for Maize Disease Classification by Mehdhar S. A. M. Al-Gaashani, Reem Alkanhel, Muthana Ali Salem Ali, Mohammed Saleh Ali Muthanna, Ahmed Aziz, Ammar Muthanna

“…However, it is highly vulnerable to various diseases such as northern leaf blight, common rust, and maize lethal necrosis, which can lead to significant crop losses if not detected early. Traditional CNN-based models, while effective in extracting spatial features, often fail to capture subtle multi-scale variations necessary for distinguishing between disease symptoms. …”
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Identification of spodumene using a remote-sensing index cube from SDGSAT-1 and other satellites by Siyuan Li, Nannan Zhang, Yong Li, Li Chen, Hao Zhang, Jinyu Chang, Jintao Tao, Jianpeng Jing

“…The model combines a convolute onal neural network (CNN) and a graph convolutional network (GCN), integrating spatial and spectral features to enhance identification accuracy. …”
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Advancing arabic dialect detection with hybrid stacked transformer models by Hager Saleh, Hager Saleh, Hager Saleh, Abdulaziz AlMohimeed, Rasha Hassan, Mandour M. Ibrahim, Saeed Hamood Alsamhi, Moatamad Refaat Hassan, Sherif Mostafa

“…The stacking model compares various models, including long-short-term memory (LSTM), gated recurrent units (GRU), convolutional neural network (CNN), and two transformer models using different word embedding. …”
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ConvXGB: A novel deep learning model to predict recurrence risk of early-stage cervical cancer following surgery using multiparametric MRI images by Ji Wu, Jian Li, Bo Huang, Sunbin Dong, Luyang Wu, Xiping Shen, Zhigang Zheng

“…We designed a novel deep learning model called “ConvXGB” for predicting recurrence risk by combining the convolutional neural network (CNN) and eXtreme Gradient Boost (XGBoost). The predictive performance of the ConvXGB model was evaluated using time-dependent area under curve (AUC), compared with the deep learning radio-clinical model, clinical model, conventional radiomics nomogram and an existing histology-specific tool. …”
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Adaptive Hierarchical Multi-Headed Convolutional Neural Network With Modified Convolutional Block Attention for Aerial Forest Fire Detection by Md. Najmul Mowla, Davood Asadi, Shamsul Masum, Khaled Rabie

“…Building upon our prior work on the Unmanned Aerial Vehicle-based Forest Fire Database (UAVs-FFDB) and the multi-headed CNN (MHCNN), this study introduces a novel architecture, namely, the Adaptive Hierarchical Multi-Headed Convolutional Neural Network with Modified Convolutional Block Attention Module (AHMHCNN-mCBAM). …”
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