Golden Chip-Free Hardware Trojan Detection Using Attention-Based Non-Local Convolution With Simple Recurrent Unit by Rama Devi Maddineni, Deepak Ch

“…In this approach, a non-local convolutional neural network embedded with an attention module plays a crucial role in efficiently extracting global spatial features from relevant regions. …”
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A Multi-Kernel Mode Using a Local Binary Pattern and Random Patch Convolution for Hyperspectral Image Classification by Wei Huang, Yao Huang, Zebin Wu, Junru Yin, Qiqiang Chen

“…First, we use LBP and hierarchical convolutional neural networks to extract local textural features and multilayer convolutional features, respectively. …”
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Acceleration of Urdu Optical Character Recognition on Zynq UltraScale+ MPSoC Using Deep Convolutional Neural Network by Fauzia Yasir, Majida Kazmi

“…This study presents a hardware-accelerated Urdu OCR framework using a custom-designed Convolutional Neural Network (CNN) optimized for deployment on the Xilinx Zynq UltraScale+ MPSoC (ZCU104). …”
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A Joint Estimation Method of Distribution Network Topology and Line Parameters Based on Power Flow Graph Convolutional Networks by Yu Wang, Xiaodong Shen, Xisheng Tang, Junyong Liu

“…An innovative joint estimation method for distribution network topology and line parameters is presented, utilizing a power flow graph convolutional network (PFGCN). This approach addresses the limitations of traditional methods that rely on costly voltage phase angle measurements. …”
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3D Reconstruction of Asphalt Pavement Macro-Texture Based on Convolutional Neural Network and Monocular Image Depth Estimation by Xinliang Liu, Chao Yin

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Automatic melanoma and non-melanoma skin cancer diagnosis using advanced adaptive fine-tuned convolution neural networks by Muhammad Amir Khan, Tehseen Mazhar, Muhammad Danish Ali, Umar Farooq Khattak, Tariq Shahzad, Mamoon M. Saeed, Habib Hamam

“…In this research, we advise an intelligent computer-aided system for automatic skin cancer diagnosis using a two-stage transfer learning approach and Pre-trained Convolutional Neural Networks (CNNs). CNNs are well-suited for learning hierarchical features from images. …”
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Development of low-cost portable spectrometer equipped with 18-band spectral sensors using deep learning model for evaluating moisture content of rubber sheets by Amorndej Puttipipatkajorn, Amornrit Puttipipatkajorn

“…While the choice of spectrometer can vary depending on its intended use, the increased cost of high-performance spectrometers may not be justified in certain applications. …”
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An Improved Scene Adaptive PRM Path Planning Algorithm by YANG Jingzhao, WANG Chao, ZHANG Yu

“…Aiming at the contradiction between fineness, dynamics and planning real-time when planning ground unmanned equipment paths in dynamic and complex environments, a ground unmanned equipment path planning algorithm based on risk fusion cost map and adaptive PRM is proposed. Firstly, the risk fusion cost map is established based on the multi-factor environment scene, the scene adaptive PRM probabilistic road map is constructed through the mechanisms of Gaussian convolution adaptive sampling, nonlinear probability enhancement and secondary sampling strategy, and the local planner is utilized to dynamically update the weights of the probabilistic road map, so as to realize the optimized search of global low-risk cost paths. …”
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A Weight Assignment-Enhanced Convolutional Neural Network (WACNN) for Freight Volume Prediction of Sea–Rail Intermodal Container Systems by Yuhonghao Wang, Wenxin Li, Xingmin Qi, Yinzhang Yu

“…In order to integrate the use of transportation resources, develop a reasonable sea–rail intermodal container transportation plan, and achieve cost reduction and efficiency improvement of the multimodal transportation system, a method for predicting the daily freight volume of sea–rail intermodal transportation based on a convolutional neural network (CNN) algorithm is proposed and a new feature processing method is used: weight assignment (WA). …”
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Efficient intelligent fault diagnosis method and graphical user interface development based on fusion of convolutional networks and vision transformers characteristics by Chaoquan Mo, Ke Huang, Houxin Ji, Wenhan Li, Kaibo Xu

“…Vision Transformers, by leveraging self-attention mechanisms to capture global dependencies, have shown excellent performance in many visual tasks, but often come with high computational costs. Therefore, this paper proposes a lightweight and efficient intelligent fault diagnosis method based on the fusion of Convolutional Network and Vision Transformer features (FCNVT). …”
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GWSC-SegMamba: Gate Wavelet Spatial Convolution Enhanced State Space Model for Multi-Temporal Agricultural Land Segmentation by Yohanes Fridolin Hestrio, Aprinaldi Jasa Mantau, Wisnu Jatmiko

“…We present GWSC-SegMamba, an innovative hybrid architecture that integrates State Space Models (SSMs) with Gate Wavelet Convolution (GWC) and Gate Spatial Convolution (GSC) components to tackle multi-temporal agricultural segmentation issues. …”
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An intelligent non-destructive method to identify the quality of self-compacting concrete based on convolutional neural networks via image recognition by Zhong Xiao, Zixuan Liu, Xuying Guo, Jun Liu

“…Sampling inspection not only cannot realize the comprehensive coverage of the inspected concrete, but also is complicated, damaging and costly. An intelligent non-destructive method to identify the quality of self-compacting concrete based on convolutional neural networks (CNN) via image recognition was proposed in this study. …”
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Deep learning-assisted screening and diagnosis of scoliosis: segmentation of bare-back images via an attention-enhanced convolutional neural network by Xingyu Duan, Xiaojun Ma, Mengqi Zhu, Linan Wang, Dingqi You, Lili Deng, Ningkui Niu

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Vibration-based gearbox fault diagnosis using a multi-scale convolutional neural network with depth-wise feature concatenation. by Van-Trang Nguyen, Quoc Bao Diep

“…This article proposes a novel approach for vibration-based gearbox fault diagnosis using a multi-scale convolutional neural network with depth-wise feature concatenation named MixNet. …”
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Development of an embedded diagnostic tool for visual misalignment screening by Daniel Soto Rodriguez, Andres Eduardo Rivera Gomez, Ruthber Rodriguez Serrezuela

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Precision-Driven Semantic Segmentation of Pipe Gallery Diseases Using PipeU-NetX: A Depthwise Separable Convolution Approach by Wenbin Song, Hanqian Wu, Chunlin Pu

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Anaemia detection in infants of 6–59 months using medical images: a multi-architecture convolutional neural network-based approach by Justice Williams Asare, Martin Mabeifam Ujakpa, Seth Alornyo, Emmanuel Akwah Kyei, Prince Modey, William Leslie Brown-Acquaye, Emmanuel Freeman, Alfred Coleman

“…This study aims to detect anaemia in children aged 6–59 months using images of the conjunctiva of the eye, employing single convolutional neural network (CNN) architectures, including AlexNet, DenseNet, and VGGNet. …”
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A Neural Network with Multiscale Convolution and Feature Attention Based on an Electronic Nose for Rapid Detection of Common Bunt Disease in Wheat Plants by Zhizhou Ren, Kun Liang, Yihe Liu, Xiaoxiao Wu, Chi Zhang, Xiuming Mei, Yi Zhang

“…This study offers a practical, rapid, and cost-effective method for monitoring and managing common bunt disease in wheat, enhancing crop protection and food security.…”
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Advancing Alzheimer’s disease detection: a novel convolutional neural network based framework leveraging EEG data and segment length analysis by Md Nurul Ahad Tawhid, Siuly Siuly, Enamul Kabir, Yan Li

“…Electroencephalography (EEG) is noninvasive, cost-effective, and has high temporal resolution, making it a promising tool for automated AD detection. …”
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Smart material selection strategies for sustainable and cost-effective high-performance concrete production using deep learning by T. Seethalakshmi, M. Murugan, P. Maria Antony Sebastin Vimalan

“…To overcome these limitations, we developed a Multi-Objective Ant Colony Adaptive Dense Convolutional Neural Network (MOAC-ADenseNet) with 5-K-Fold cross validation, a dependable and precise forecasting model for the cost-effective selection of HPC material. …”
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