Quantum classical hybrid convolutional neural networks for breast cancer diagnosis by Qiuyu Xiang, Dongfen Li, Zhikang Hu, Yuhang Yuan, Yuchen Sun, Yonghao Zhu, You Fu, Yangyang Jiang, Xiaoyu Hua

“…However, the efficacy and cost limitations of conventional diagnostic techniques increase the possibility of misdiagnosis. …”
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A Residual Optronic Convolutional Neural Network for SAR Target Recognition by Ziyu Gu, Zicheng Huang, Xiaotian Lu, Hongjie Zhang, Hui Kuang

“…However, huge computational costs and power consumption are challenging the development of current DL methods. …”
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Crushed Stone Grain Shapes Classification Using Convolutional Neural Networks by Alexey N. Beskopylny, Evgenii M. Shcherban’, Sergey A. Stel’makh, Irina Razveeva, Alexander L. Mailyan, Diana Elshaeva, Andrei Chernil’nik, Nadezhda I. Nikora, Gleb Onore

“…These models provide for the complete automation of crushed stone grain typing, leading to reduced labor costs and a decreased likelihood of human error.…”
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PS-YOLO: A Lighter and Faster Network for UAV Object Detection by Han Zhong, Yan Zhang, Zhiguang Shi, Yu Zhang, Liang Zhao

“…GSCD employs shared convolutions to enhance the network’s ability to learn common features across objects of different scales and introduces Normalized Gaussian Wasserstein Distance Loss (NWDLoss) to improve detection accuracy. …”
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A Lightweight Deep Learning Model for Profiled SCA Based on Random Convolution Kernels by Yu Ou, Yongzhuang Wei, René Rodríguez-Aldama, Fengrong Zhang

“…Determining how to design a lightweight deep learning model that can handle a trace with more power points and has fewer parameters and lower time costs for profiled SCAs appears to be a challenge. …”
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Congestion Management Using an Optimized Deep Convolution Neural Network in Deregulated Environment by Dhanadeepika B., Vanithasri M., Chakravarthi M.

“…The most important results for the test system indicating convergence profile, congestion cost, and change in real-power and voltage magnitude are obtained by the simulation in MATLAB, and on the basis of the obtained simulation outcomes, it is evident that the proposed Improved Lion Algorithm optimized Deep Convolution Neural Network displays phenomenal computation performance in minimizing congestion losses at minimum congestion costs. …”
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Inverse Problems for a Parabolic Integrodifferential Equation in a Convolutional Weak Form by Kairi Kasemets, Jaan Janno

“…We deduce formulas for the Fréchet derivatives of cost functionals of several inverse problems for a parabolic integrodifferential equation in a weak formulation. …”
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A REVIEW OF CONVOLUTIONAL NEURAL NETWORK IN EMERGING TRENDS AND OPPORTUNITIES IN PRECISION AGRICULTURE by Amit Hasan Sadhin, Reshad Rayhan

“…Food security has become a significant issue over the last few decades. Convolutional Neural Networks familiarize new sensations in precision agriculture; based on this, researchers have introduced effective planning, organized cultivation, smart irrigation, faster production, and cost reduction to address the continuously increasing demand for food supplies and to improve environmental as well as food sustainability. …”
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Convolutional neural network model over encrypted data based on functional encryption by Chen WANG, Jiarun LI, Jian XU

“…Currently, homomorphic encryption, secure multi-party computation, and other encryption schemes are used to protect the privacy of sensitive data in outsourced convolutional neural network (CNN) models.However, the computational and communication overhead caused by the above schemes would reduce system efficiency.Based on the low cost of functional encryption, a new convolutional neural network model over encrypted data was constructed using functional encryption.Firstly, two algorithms based on functional encryption were designed, including inner product functional encryption and basic operation functional encryption algorithms to implement basic operations such as inner product, multiplication, and subtraction over encrypted data, reducing computational and communication costs.Secondly, a secure convolutional computation protocol and a secure loss optimization protocol were designed for each of these basic operations, which achieved ciphertext forward propagation in the convolutional layer and ciphertext backward propagation in the output layer.Finally, a secure training and classification method for the model was provided by the above secure protocols in a module-composable way, which could simultaneously protect the confidentiality of user data as well as data labels.Theoretical analysis and experimental results indicate that the proposed model can achieve CNN training and classification over encrypted data while ensuring accuracy and security.…”
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Hierarchical Convolution-Transformer Framework for Gear Fault Diagnosis Under Severe Noise by Qiushi He, Bo Kang, Shanshan Fan, Xueyi Li

“…To address the limitations of convolutional neural networks in capturing global fault features, the high computational cost and overfitting risk of Transformer models in gear fault diagnosis, and the feature degradation under strong noise, this study proposes a novel convolution-Transformer–channel attention network. …”
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ClipQ: Clipping Optimization for the Post-Training Quantization of Convolutional Neural Network by Yiming Chen, Hui Zhang, Chen Zhang, Yi Liu

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Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease by Uday Pratap Singh, Siddharth Singh Chouhan, Sukirty Jain, Sanjeev Jain

“…Therefore, for this paper, a multilayer convolutional neural network (MCNN) is proposed for the classification of the Mango leaves infected by the Anthracnose fungal disease. …”
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Convolutional neural network analysis of optical texture patterns in liquid-crystal skyrmions by J. Terroa, M. Tasinkevych, C. S. Dias

“…Our method focuses specifically on the skyrmion-localised regions, reducing significantly the computational cost. By training convolutional neural networks on simulated polarised optical microscopy images of liquid crystal skyrmions, we showcase the ability of trained networks to accurately predict several selected parameters such as the free energy, cholesteric pitch, and strength of applied electric fields. …”
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PERFORMANCE REFINEMENT OF CONVOLUTIONAL NEURAL NETWORK ARCHITECTURES FOR SOLVING BIG DATA PROBLEMS by Saud Aljaloud

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Improvement of a Subpixel Convolutional Neural Network for a Super-Resolution Image by Muhammed Fatih Ağalday, Ahmet Çinar

“…Since the super-resolution process is performed in the high-resolution area, it adds a memory cost and computational complexity. In our proposed model, a low-resolution image is given as input to a convolutional neural network to reduce computational complexity. …”
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Pesticide Residue Detection in Broccoli Based on Hyperspectral Technology and Convolutional Neural Network by Dan WANG, Yuqing LUAN, Zuojun TAN, Wei WEI

“…The detection of pesticide residues in agricultural products is an important step in ensuring the food safety of agricultural products, while traditional detection methods are cumbersome and costly. Using broccoli as a sample, this article used hyperspectral technology combined with machine learning algorithms and deep learning algorithms to provide a simple, fast, low-cost, and non-destructive method for detecting pesticide residues in broccoli. …”
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Optimizing skin cancer screening with convolutional neural networks in smart healthcare systems. by Ali Raza, Akhtar Ali, Sami Ullah, Yasir Nadeem Anjum, Basit Rehman

“…Algorithms applied in convolutional neural network (CNN) could lead to an enhanced speed of identifying and distinguishing a disease, which in turn leads to early detection and treatment. …”
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Traffic Scene Depth Analysis Based on Depthwise Separable Convolutional Neural Network by Jianzhong Yuan, Wujie Zhou, Sijia Lv, Yuzhen Chen

“…Subsequently, features containing advanced depth information were extracted using a block based on an ensemble of convolution layers and a block based on depth separable convolution layers. …”
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A composite improved attention convolutional network for motor imagery EEG classification by Wenzhe Liao, Zipeng Miao, Shuaibo Liang, Linyan Zhang, Chen Li

“…CIACNet utilizes a dual-branch convolutional neural network (CNN) to extract rich temporal features, an improved convolutional block attention module (CBAM) to enhance feature extraction, temporal convolutional network (TCN) to capture advanced temporal features, and multi-level feature concatenation for more comprehensive feature representation.ResultsThe CIACNet model performs well on both the BCI IV-2a and BCI IV-2b datasets, achieving accuracies of 85.15 and 90.05%, respectively, with a kappa score of 0.80 on both datasets. …”
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Real-Time Road Crack Mapping Using an Optimized Convolutional Neural Network by M-Mahdi Naddaf-Sh, SeyedSaeid Hosseini, Jing Zhang, Nicholas A. Brake, Hassan Zargarzadeh

“…A descriptive approach is considered for identifying cracks from collected images using a convolutional neural network (CNN) that classifies several types of cracks. …”
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