A Novel Approach for Enhancing Code Smell Detection Using Random Convolutional Kernel Transform by Mostefai Abdelkader, Mekour Mansour

“… Context: In software engineering, the presence of code smells is closely associated with increased maintenance costs and complexities, making their detection and remediation an important concern. …”
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Multi-convolutional neural networks for cotton disease detection using synergistic deep learning paradigm. by Afira Aslam, Syed Muhammad Usman, Muhammad Zubair, Amanullah Yasin, Muhammad Owais, Irfan Hussain

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Testing convolutional neural network based deep learning systems: a statistical metamorphic approach by Faqeer ur Rehman, Clemente Izurieta

“…Conventional metamorphic testing techniques have certain limitations in verifying deep learning-based models (i.e., convolutional neural networks (CNNs)) that have a stochastic nature (because of randomly initializing the network weights) in their training. …”
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Computationally Efficient Single Layer Transformer Convolutional Encoder for Accurate Price Prediction of Agriculture Commodities by Caceja Elyca Anak Bundak, Mohd Amiruddin Abd Rahman, Nurin Syazwina Mohd Haniff, Nur Syaiful Afrizal, Khairul Adib Yusof, Muhammad Khalis Abdul Karim, Md Shuhazlly Mamat, Romi Fadillah Rahmat

“…Therefore, this study introduces the single-layer Transformer Convolutional Encoder algorithm (STCE), an improved version of the traditional transformer encoder. …”
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MmNet: Identifying Mikania micrantha Kunth in the wild via a deep Convolutional Neural Network by Xi QIAO, Yan-zhou LI, Guang-yuan SU, Hong-kun TIAN, Shuo ZHANG, Zhong-yu SUN, Long YANG, Fang-hao WAN, Wan-qiang QIAN

“…The network consists of AlexNet Local Response Normalization (LRN), along with the GoogLeNet and continuous convolution of VGG inception models. After training and testing, the identification of 400 testing samples by MmNet is very good, with accuracy of 94.50% and time cost of 10.369 s. …”
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Lightweight graph convolutional network with multi-attention mechanisms for intelligent action recognition in online physical education by Yuhao You

“…To address this, we propose a lightweight graph convolutional network (GCN) that integrates an improved Ghost module with multi-attention mechanisms, including a global attention mechanism (GAM) and a channel attention mechanism (CAM), to enhance spatial and temporal feature extraction. …”
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Enhanced Coronary Artery Disease Classification Through Feature Engineering and One-Dimensional Convolutional Neural Network by Atitaya Phoemsuk, Vahid Abolghasemi

“…The proposed method works based on a one-dimensional convolutional neural network (1D-CNN), offering a cost-effective alternative for sophisticated cardiac health monitoring. …”
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Bone Segmentation in Low-Field Knee MRI Using a Three-Dimensional Convolutional Neural Network by Ciro Listone, Diego Romano, Marco Lapegna

“…This study proposes an automated segmentation method based on a 3D U-Net convolutional neural network to segment the femur, tibia, and patella from low-field MRI scans. …”
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Residual capsule network with threshold convolution and attention mechanism for forest fire detection using UAV imagery by Soufiane Ben Othman, Obaid Ali

“…This paper introduces ResCaps-TC-Attn-Fire, a novel deep learning framework tailored for UAV-based forest fire detection, combining Residual-Capsule Networks, Threshold Convolution, and Attention Mechanisms. Residual-Capsule Networks enhance the capture of spatial hierarchies and inter-feature relationships, improving robustness to diverse fire characteristics, while Threshold Convolution filters irrelevant features to boost generalization and efficiency. …”
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LS-MambaNet: Integrating Large Strip Convolution and Mamba Network for Remote Sensing Object Detection by Lingyu Yan, Zijian He, Zhiqi Zhang, Guangqi Xie

“…Specifically, firstly, a group fusion strategy is combined with the introduction of large-band convolution to adaptively adjust the receptive domains of the features, which enhances the spatial context information extraction for objects with high aspect ratios. …”
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Ship crack detection method based on lightweight fast convolution and bidirectional weighted feature fusion network by Chong WANG, Yuhui ZHU

“…Methods First, a lightweight convolutional structure (GSConv) is used to replace the standard convolution and introduce an attention mechanism in the backbone of YOLOv5s to achieve the reduction of network parameters and computational complexity while enhancing the ability to extract crack features. …”
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Convolutional Versus Large Language Models for Software Log Classification in Edge-Deployable Cellular Network Testing by Achintha Ihalage, Sayed Taheri, Faris Muhammad, Hamed Al-Raweshidy

“…These include a constrained context window, limited applicability to text beyond natural language, and high inference costs. To address these limitations, we propose a compact convolutional neural network (CNN) architecture that offers a context window spanning up to 200,000 characters and achieves over 96% accuracy (F<inline-formula> <tex-math notation="LaTeX">$1\gt 0.9$ </tex-math></inline-formula>) in classifying multifaceted software logs into various layers in the telecommunications protocol stack. …”
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p-im2col: Simple Yet Efficient Convolution Algorithm With Flexibly Controlled Memory Overhead by Anton V. Trusov, Elena E. Limonova, Dmitry P. Nikolaev, Vladimir V. Arlazarov

“…However, commonly used GeMM-based algorithms may cause significant memory overhead or avoid it only at the cost of worse performance. In this paper, we propose a novel convolution algorithm, p-im2col, based on a well-known im2col algorithm that avoids memory overhead by splitting a single multiplication of a large matrix into several multiplications of smaller matrices. …”
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Recognition of Knee Osteoarthritis by 1D and 2D Convolutional Neural Networks Using Vibroarthrographic Signals by Jia-Jung Wang, Alok Kumar Sharma, Shing-Hong Liu, Wenxi Chen, Cheng-Yo Yen

“…This study utilized 1D and 2D convolutional neural networks (CNN) to assess OA of the knee using vibroarthrographic (VAG) signals recorded by an inertial measurement unit sensor. …”
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Classification of Short-Segment Pediatric Heart Sounds Based on a Transformer-Based Convolutional Neural Network by Md Hassanuzzaman, Samit Kumar Ghosh, Mohammad Nurul Akhtar Hasan, Mohammad Abdullah Al Mamun, Khawza I. Ahmed, Raqibul Mostafa, Ahsan H. Khandoker

“…Mel-frequency cepstral coefficients (MFCCs) are extracted as features and fed into a transformer-based residual one-dimensional convolutional neural network (1D-CNN) for classification. …”
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RF-AIRCGR: Lightweight Convolutional Neural Network-Based RFID Chinese Character Gesture Recognition Research by Yajun Zhang, Congcong Wang, Feng Li, Weiqian Yu, Yuankang Wang, Jingying Chen

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MEAC: A Multi-Scale Edge-Aware Convolution Module for Robust Infrared Small-Target Detection by Jinlong Hu, Tian Zhang, Ming Zhao

“…To overcome these limitations, we propose a Multi-Scale Edge-Aware Convolution (MEAC) module that enhances feature representation for small infrared targets without increasing parameter count or computational cost. …”
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High-Resolution Geochemical Data Mapping With Swin Transformer-Convolution-Based Multisource Geoscience Data Fusion by Ye Yuan, Shuguang Zhou, Jianhua Bian, Jinlin Wang, Wei Han, Jining Yan

“…However, the high economic cost of geochemical data analysis hinders large-scale studies, leading to low spatial resolution, especially in remote areas. …”
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Use of a convolutional neural network for direct detection of acid-fast bacilli from clinical specimens by Paul English, Muir J. Morrison, Blaine Mathison, Elizabeth Enrico, Ryan Shean, Brendan O'Fallon, Deven Rupp, Katie Knight, Alexandra Rangel, Jeffrey Gilivary, Amanda Vance, Haleina Hatch, Leo Lin, David P. Ng, Salika M. Shakir

“…We present the development of an artificial intelligence computer vision process using a deep convolutional neural network to detect acid-fast bacilli (AFB) from Kinyoun acid-fast stained slides. …”
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Guarded Diagnosis: Preserving Privacy in Cervical Cancer Detection with Convolutional Neural Networks on Pap Smear Images by Sanmugasundaram Ravichandran, Hui-Kai Su, Wen-Kai Kuo, Manikandan Mahalingam, Kanimozhi Janarthanan, Kabilan Saravanan, Bruhathi Sathyanarayanan

“…Using a convolutional neural network (CNN) and the SIPaKMeD dataset, cervical cells are classified into normal, precancerous, and benign cells after segmentation. …”
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