Detection of Diabetic Retinopathy Using Bichannel Convolutional Neural Network by Shu-I Pao, Hong-Zin Lin, Ke-Hung Chien, Ming-Cheng Tai, Jiann-Torng Chen, Gen-Min Lin

“…Deep learning of fundus photograph has emerged as a practical and cost-effective technique for automatic screening and diagnosis of severer diabetic retinopathy (DR). …”
Get full text

Classification of Some Barley Cultivars with Deep Convolutional Neural Networks by Fatih Bayram, Mustafa Yıldız

“…Six different deep convolutional neural network models were designed based on a transfer learning method with pretrained DenseNet-121, DenseNet-169, DenseNet-201, InceptionResNetV2, MobileNetV2 and Xception networks. …”
Get full text

Diagnosis of Tuberculosis Using Medical Images by Convolutional Neural Networks by Abolfazl Pordeli Shahreki, Fatemeh Sadat Hosseini-Baharanchi, Masoud Roudbari

“…Background: One of the best ways to reduce the spread of tuberculosis (TB) is to diagnose the disease using chest X-ray (CXR) images as a low-cost and affordable method. However, there are two problems: the lack of adequate radiologists and the possibility of misdiagnosis. …”
Get full text

An Efficient Group Convolution and Feature Fusion Method for Weed Detection by Chaowen Chen, Ying Zang, Jinkang Jiao, Daoqing Yan, Zhuorong Fan, Zijian Cui, Minghua Zhang

“…This model introduces plug-and-play modules: (1) The Efficient Group Convolution (EGC) module leverages convolution kernels of various sizes combined with group convolution techniques to significantly reduce computational cost. …”
Get full text

Application of Convolutional Neural Networks in Animal Husbandry: A Review by Rotimi-Williams Bello, Roseline Oluwaseun Ogundokun, Pius A. Owolawi, Etienne A. van Wyk, Chunling Tu

“…Convolutional neural networks (CNNs) and their application in animal husbandry have in-depth mathematical expressions, which usually revolve around how well they map input data such as images or video frames of animals to meaningful outputs like health status, behavior class, and identification. …”
Get full text

Convolutional Variational Autoencoder for Anomaly Detection in On-Load Tap Changers by Fataneh Dabaghi-Zarandi, Hassan Ezzaidi, Michel Gauvin, Patrick Picher, Issouf Fofana, Vahid Behjat

“…To detect anomalies in OLTCs and analyze the generated vibration signals, a convolutional variational autoencoder (CVAE) is utilized, trained individually for each transformer family. …”
Get full text

A New Support Vector Machine Based on Convolution Product by Wei-Chang Yeh, Yunzhi Jiang, Shi-Yi Tan, Chih-Yen Yeh

“…Nonetheless, smaller datasets may be very important, costly, and not easy to obtain in a short time. This paper proposes a novel convolutional SVM (CSVM) that has the advantages of both CNN and SVM to improve the accuracy and effectiveness of mining smaller datasets. …”
Get full text

Faster Training of Large Kernel Convolutions on Smaller Spatial Scales by Shota Fukuzaki, Masaaki Ikehara

“…This study aims to accelerate the training of the large kernel convolutions by resizing both training images and convolution filters to a smaller scale. …”
Get full text

Application of improved graph convolutional network for cortical surface parcellation by Jia Tan, Xiaomei Ren, Yong Chen, Xianju Yuan, Feiba Chang, Rui Yang, Chengqun Ma, Xiaoyu Chen, Miao Tian, Wei Chen, Zihong Wang

“…In this study, we propose an Attention-guided Deep Graph Convolutional network (ADGCN) for end-to-end parcellation on primitive cortical surface manifolds. …”
Get full text

Magnetic Moment Estimation Algorithm Based on Convolutional Neural Network by Xiuzhi You, Junqian Zhang, Bingyang Chen, Ke Zhang, Xiaodong Liu, Bin Yan, Wanhua Zhu

“…Current methods primarily rely on vector magnetic field signals or gradient signals, which face challenges such as steering errors and the high costs associated with sensor arrays. This paper proposes a magnetic moment estimation algorithm that combines a scalar magnetic field sensor and the three components of the local geomagnetic field with a convolutional neural network (CNN). …”
Get full text

Large-Scale Video Retrieval via Deep Local Convolutional Features by Chen Zhang, Bin Hu, Yucong Suo, Zhiqiang Zou, Yimu Ji

“…A novel framework based on convolutional neural networks (CNNs) is proposed to perform large-scale video retrieval with low storage cost and high search efficiency. …”
Get full text

Hierarchical Classification of Variable Stars Using Deep Convolutional Neural Networks by Mahdi Abdollahi, Nooshin Torabi, Sadegh Raeisi, Sohrab Rahvar

“…All the models in both steps have same network structure and we test both Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). …”
Get full text

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

“…To address these issues, this paper adds a quantum convolutional layer to the classical convolutional neural network to take advantage of quantum computing to improve learning efficiency and processing speed. …”
Get full text

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. …”
Get full text

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

“…This study implements methods using convolutional neural networks, which solve an important problem in the construction industry—to classify crushed stone grains by their shape. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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.…”
Get full text

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. …”
Get full text