CN2VF-Net: A Hybrid Convolutional Neural Network and Vision Transformer Framework for Multi-Scale Fire Detection in Complex Environments by Naveed Ahmad, Mariam Akbar, Eman H. Alkhammash, Mona M. Jamjoom

“…This paper proposes the CN2VF-Net model, a novel hybrid architecture that combines vision Transformers (ViTs) and convolutional neural networks (CNNs), effectively addressing these challenges. …”
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Artificial intelligence assisted common maternal fetal planes prediction from ultrasound images based on information fusion of customized convolutional neural networks by Fatima Rauf, Muhammad Attique Khan, Hussain M. Albarakati, Kiran Jabeen, Shrooq Alsenan, Ameer Hamza, Sokea Teng, Yunyoung Nam

“…Two novel deep learning architectures have been designed in the proposed architecture based on 3-residual and 4-residual blocks with different convolutional filter sizes. The hyperparameters of the proposed architectures were initialized through Bayesian Optimization. …”
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Deep learning and capsule endoscopy: automatic identification and differentiation of small bowel lesions with distinct haemorrhagic potential using a convolutional neural network by Guilherme Macedo, Miguel José Mascarenhas Saraiva, João Afonso, Tiago Ribeiro, João Ferreira, Helder Cardoso, Ana Patricia Andrade, Marco Parente, Renato Natal, Miguel Mascarenhas Saraiva

“…Convolutional neural networks (CNNs) are highly efficient artificial intelligence tools for image analysis. …”
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Improved crop row detection by employing attention-based vision transformers and convolutional neural networks with integrated depth modeling for precise spatial accuracy by Hassan Afzaal, Derek Rude, Aitazaz A. Farooque, Gurjit S. Randhawa, Arnold W. Schumann, Nicholas Krouglicof

“…The proposed framework employs the latest attention and convolution-based encoders, such as ConvFormer, CAFormer, Swin Transformer, and ConvNextV2, in precisely identifying crop rows across varied and challenging agricultural environments. …”
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Rapid detection of acetolactate synthase inhibitor–resistant weeds using novel full-spectrum imaging and a hyperparameter-tuned convolutional neural network by Pauline Victoria Estrada, John Benedict Estrada, Jennifer Valdez-Herrera, Anil Shrestha

“…This novel approach exploits the subtle differences in the spectral signature of ALS inhibitor-resistant and ALS inhibitor-susceptible common chickweed plants as they react differently to the ALS-inhibiting herbicide treatments. …”
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Early detection of thermal image based T1 breast cancer using enhanced multiwavelet denoised convolution neural network with region based analysis by P. Geetha, S. UmaMaheswari

“…Amplified cancer cell images are analyzed using the proposed Multiwavelet-Deep Denoised Convolutional Neural Network (MWTDnCNN) algorithm for T1 cancer cell detection. …”
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Breast Tumor-Like-Masses Segmentation From Scattering Images Obtained With an Ultrahigh-Sensitivity Talbot-Lau Interferometer Using Convolutional Neural Networks by Ionut-Cristian Ciobanu, Nicoleta Safca, Elena Anghel, Dan Popescu

“…Conventional mammography methods still have some limitations in differentiating between healthy and tumorous tissue, particularly in cases with low-density differences. This study investigates the potential of combining ultrahigh-sensitivity Talbot-Lau interferometry with Convolutional Neural Networks (CNNs) to enhance breast tumor segmentation from scattering images. …”
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Comparative Analysis of Machine Learning Based Approaches for Face Detection and Recognition by Ratnesh Kumar Shukla, Arvind Kumar Tiwari

“…We also show that deeper discrimination can be learned by creating a deep network that retains inter-cluster differences both and within groups. MobileNet, which provides both offline and real-time precision and speed to provide fast and consistent stable results, is the recently suggested Convolutional Neural network (CNN) model. …”
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PDSDC: Progressive Spatiotemporal Difference Capture Network for Remote Sensing Change Detection by YeKai Cui, Peng Duan, Jinjiang Li

“…To address these issues, this article proposes a progressive spatiotemporal difference capture network. This framework effectively mitigates information degradation and multiscale tradeoffs in feature fusion through a multipath optimization mechanism. …”
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FLEDNet: Enhancing the Drone Classification in the Radio Frequency Domain by Boban Sazdic-Jotic, Milenko Andric, Boban Bondzulic, Slobodan Simic, Ivan Pokrajac

“…Researchers are actively pursuing advancements in convolutional neural networks and their application in anti-drone systems for drone classification tasks. …”
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Enhancing human–robot collaboration with thermal images and deep neural networks: the unique thermal industrial dataset WLRI-HRC and evaluation of convolutional neural networks by S. Süme, K.-M. Ponomarjova, T. M. Wendt, S. J. Rupitsch

“…In this research, the dataset is evaluated for implementation by different convolutional neural networks: first, one-stage methods, i.e., You Only Look Once (YOLO v5, v8, v9 and v10) in different model sizes and, secondly, two-stage methods with Faster R-CNN with three variants of backbone structures (ResNet18, ResNet50 and VGG16). …”
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Novel non-destructive authentication of nine Dendrobium species using residual convolutional neural network relying on plant images and FT-NIR spectral information by Yulin Xu, Lian Li, Yuanzhong Wang, Qiang Hu

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Deep Learning-Based Glaucoma Detection Using Clinical Notes: A Comparative Study of Long Short-Term Memory and Convolutional Neural Network Models by Ali Mohammadjafari, Maohua Lin, Min Shi

“…<b>Methods:</b> We compared multiple deep learning architectures, including Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and transformer-based models BERT and BioBERT. …”
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A GPSR-Based Sparse ECG Signal Recovery Approach for Hybrid Compressive Sensing Framework and Its Classification Using Deep Convolution Neural Network by Subramanyam Shashi Kumar, Prakash Ramachandran

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Automated learning of glaucomatous visual fields from OCT images using a comprehensive, segmentation-free 3D convolutional neural network model by Makoto Koyama, Yuta Ueno, Yoshikazu Ito, Tetsuro Oshika, Masaki Tanito

“…Abstract A segmentation-free 3D Convolutional Neural Network (3DCNN) model was adopted to estimate Visual Field (VF) in glaucoma cases using Optical Coherence Tomography (OCT) images. …”
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Deep convolutional neural network based archimedes optimization algorithm for heart disease prediction based on secured IoT enabled health care monitoring system by Sureshkumar S, Santhosh Babu A. V, Joseph James S, Maranco M

“…Finally, the Deep Convolutional Neural Network-based Archimedes Optimization (DCNN-AO) algorithm classifies the normal and abnormal classes of heart disease. …”
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Dynamic Path Flow Estimation Using Automatic Vehicle Identification and Probe Vehicle Trajectory Data: A 3D Convolutional Neural Network Model by Can Chen, Yumin Cao, Keshuang Tang, Keping Li

“…Then, a three-dimensional (3D) convolutional neural network (CNN) model is designed to exploit the hidden patterns from the tensor and establish the high-dimensional correlations with path flows. …”
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Cloud based real-time multivariate multi-step prediction of systolic blood pressure and heart rate using temporal convolutional network and Apache Spark by Hager Saleh, Nora El-Rashidy, Sherif Mostafa, Abdulaziz AlMohimeed, Shaker El-Sappagh, Zainab H. Ali

“…During the offline model development, we explore the single-task and multi-task modeling. Different optimization steps have been explored. The single task includes forecasting HR and SBP in multi-step heads using Temporal Convolutional Networks (TCN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). …”
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Accuracy of Diabetic Retinopathy Staging with a Deep Convolutional Neural Network Using Ultra-Wide-Field Fundus Ophthalmoscopy and Optical Coherence Tomography Angiography by Toshihiko Nagasawa, Hitoshi Tabuchi, Hiroki Masumoto, Shoji Morita, Masanori Niki, Zaigen Ohara, Yuki Yoshizumi, Yoshinori Mitamura

“…The present study aimed to compare the accuracy of diabetic retinopathy (DR) staging with a deep convolutional neural network (DCNN) using two different types of fundus cameras and composite images. …”
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WSDC-ViT: a novel transformer network for pneumonia image classification based on windows scalable attention and dynamic rectified linear unit convolutional modules by Yu Gu, Haotian Bai, Meng Chen, Lidong Yang, Baohua Zhang, Jing Wang, Xiaoqi Lu, Jianjun Li, Xin Liu, Dahua Yu, Ying Zhao, Siyuan Tang, Qun He

“…Abstract Accurate differential diagnosis of pneumonia remains a challenging task, as different types of pneumonia require distinct treatment strategies. …”
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