EA-CNN: Enhanced attention-CNN with explainable AI for fruit and vegetable classification by Zeshan Aslam Khan, Muhammad Waqar, Khalid Mehmood Cheema, Ali Abu Bakar Mahmood, Quratul Ain, Naveed Ishtiaq Chaudhary, Abdullah Alshehri, Sultan S. Alshamrani, Muhammad Asif Zahoor Raja

“…In this study, an explainable artificial intelligence (XAI) driven enhanced attention-CNN (EA-CNN) is proposed for accomplishing the fruit and vegetable classification task accurately and efficiently. …”
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BSO-CNN: A BSO Pressure Optimized CNN Model for Water Distribution Networks by Waghmare Shwetambari Pandurang, Renu Praveen Pathak, Imtiyaz Ahmad Wani

Subjects: “…Convolution Neural Network (CNN)…”
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CNN for Computer Vision tasks by Р. Ковальчук, О. Польшакова

“…Different types of convolutional operations (expanded, partial, strided) and various CNN models (LeNet, AlexNet, VGGNet, GoogLeNet, ResNet) are also examined. …”
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“CNN Can Kiss My As$” by Andrea Lorenz, Carolyn Schmitt, Shannon McGregor, Daniel Malmer

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MSRD-CNN: Multi-Scale Residual Deep CNN for General-Purpose Image Manipulation Detection by Kapil Rana, Gurinder Singh, Puneet Goyal

“…In this paper, a novel Multi-Scale Residual Deep CNN (MSRD-CNN) is designed to learn the image manipulation features adaptively for multiple image manipulation detection. …”
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Speech Emotion Recognition: Comparative Analysis of CNN-LSTM and Attention-Enhanced CNN-LSTM Models by Jamsher Bhanbhro, Asif Aziz Memon, Bharat Lal, Shahnawaz Talpur, Madeha Memon

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DHS-CNN: A Defect-Adaptive Hierarchical Structure CNN Model for Detecting Anomalies in Contact Lenses by Sung-Hoon Kim, Seong-Jong Joo, Kwan-Hee Yoo

“…This paper introduces a novel defect-adaptive hierarchical structure convolution neural network (DHS-CNN) model based on InceptionV4. The proposed model architecture reflects the manufacturing process and defect types, and we developed a custom loss function to suit this multi-output hierarchical design. …”
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Hybrid CNN-based Recommendation System by Muhammad Alrashidi, Roliana Ibrahim, Ali Selamat

Subjects: “…CNN, deep learning, Recommendation systems, Social networks, Social recommendation…”
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Brain Age Estimation from MRI Images using 2D-CNN instead of 3D-CNN by Muazzez Buket Darıcı, Şüheda Yıldırım, Murat Gezer

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XABH-CNN-GRU: Explainable attention-based hybrid CNN-GRU model for accurate identification of common arrhythmias by Abduljabbar S. Ba Mahel, Fahad Mushabbab G. Alotaibi, Zenebe Markos Lonseko, Ni-Ni Rao

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PNTM-CNN: an approach for saddle-point extraction integrating positive–negative terrain method and multiscale fusion CNN model by Zhe Zhou, Hao Wu, Zhenyu Zhang, Bo Kong, Min Yang, Tinghua Ai, Huafei Yu

“…To address this challenge, this study presents a novel model that combines the PNTM with a convolutional neural network (CNN) called PNTM-CNN. In this approach, candidate saddle points are first identified using the PNTM and then refined using a CNN that integrates multiscale topographic features. …”
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CNN-Based Classification of Optically Critical Cutting Tools with Complex Geometry: New Insights for CNN-Based Classification Tasks by Mühenad Bilal, Ranadheer Podishetti, Tangirala Sri Girish, Daniel Grossmann, Markus Bregulla

Subjects: “…machining tools: Grad-CAM CNN…”
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Denoising and Recognition Method for Weak Acoustic Abnormal Signals in Hot-Wall Hydrogenation Reactors Using DnCNN-CNN by Xueqin Wang, Shilin Xu, Yun Tu, Ying Zhang, Mingguo Peng

“…To address this issue, this study proposes a deep double convolutional neural network that combines denoising convolutional neural networks (DnCNN) and convolutional neural networks (CNN) for denoising and recognition of weak abnormal AE signals under strong noise conditions. …”
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Integrated deep learning paradigm for comprehensive lung cancer segmentation and classification using mask R-CNN and CNN models by Abimbola G. Akintola, Kolawole Y. Obiwusi, Yusuf O. Olatunde, Mohammed Usman, Faizol G. Aberuagba, Muhammed A. Adebisi, Shamsudeen A. Adebayo

Subjects: “…Mask R-CNN…”
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Comparative Study of CNN Architectures for Brain Tumor Classification Using MRI: Exploring GradCAM for Visualizing CNN Focus by Areli Chinga, Wilden Bendezu, Antonio Angulo

“…Currently, diagnostic accuracy is limited, therefore, our approach uses five different CNN architectures to accurately identify and classify affected brain regions, specifically glioma, meningioma, or pituitary tumors. …”
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CNN-SENet: a GNSS-R ocean wind speed retrieval model integrating CNN and SENet attention mechanism by Yimin Xia, Dongliang Guan, Zhiling Zhou

“…To address this issue, this paper proposes a CNN model that incorporates the Squeeze-and-Excitation Network (SENet) attention mechanism, named CNN-SENet. …”
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CNN-based Gender Prediction in Uncontrolled Environments by Kazım Yıldız, Engin Güneş, Anil Bas

Subjects: “…cnn…”
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Relation extraction based on CNN and Bi-LSTM by Xiaobin ZHANG, Fucai CHEN, Ruiyang HUANG

“…Relation extraction aims to identify the entities in the Web text and extract the implicit relationships between entities in the text.Studies have shown that deep neural networks are feasible for relation extraction tasks and are superior to traditional methods.Most of the current relation extraction methods apply convolutional neural network (CNN) and long short-term memory neural network (LSTM) methods.However,CNN just considers the correlation between consecutive words and ignores the correlation between discontinuous words.On the other side,although LSTM takes correlation between long-distance words into account,the extraction features are not sufficiently extracted.In order to solve these problems,a relation extraction method that combining CNN and LSTM was proposed.three methods were used to carry out the experiments,and confirmed the effectiveness of these methods,which had some improvement in F1 score.…”
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Improving CNN Fish Detection and Classification with Tracking by Boubker Zouin, Jihad Zahir, Florian Baletaud, Laurent Vigliola, Sébastien Villon

“…Our method fused CNNs and tracking methods, allowing us to detect 12% more individuals compared to CNN alone.…”
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AdaptiveSwin-CNN: Adaptive Swin-CNN Framework with Self-Attention Fusion for Robust Multi-Class Retinal Disease Diagnosis by Imran Qureshi

“…The results highlight AdaptiveSwin-CNN as a robust and computationally efficient decision-support system.…”
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