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Journal articles on the topic 'Convolution Neural Networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Liao, Shengbin, Xiaofeng Wang, and ZongKai Yang. "A heterogeneous two-stream network for human action recognition." AI Communications 36, no. 3 (2023): 219–33. http://dx.doi.org/10.3233/aic-220188.

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The most widely used two-stream architectures and building blocks for human action recognition in videos generally consist of 2D or 3D convolution neural networks. 3D convolution can abstract motion messages between video frames, which is essential for video classification. 3D convolution neural networks usually obtain good performance compared with 2D cases, however it also increases computational cost. In this paper, we propose a heterogeneous two-stream architecture which incorporates two convolutional networks. One uses a mixed convolution network (MCN), which combines some 3D convolutions

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Sapunov, V. V., S. A. Botman, G. V. Kamyshov, and N. N. Shusharina. "Application of Convolution with Periodic Boundary Condition for Processing Data from Cylindrical Electrode Arrays." INFORMACIONNYE TEHNOLOGII 27, no. 3 (2021): 125–31. http://dx.doi.org/10.17587/it.27.125-131.

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In this paper, modification of convolutional neural networks for purposes of processing electromyographic data obtained from cylindrical arrays of electrodes was proposed. Taking into account the spatial symmetry of the array, convolution operation was redefined using periodic boundary conditions, which allowed to construct a neural network that is invariant to rotations of electrodes array around its axis. Applicability of the proposed approach was evaluated by constructing a neural network containing a new type of convolutional layer and training it on the open UC2018 DualMyo dataset in orde

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Yang, Dengjie, Zhiwei Chen, Yi Sun, Qing He, Shiwei Ye, and Deyuan Chen. "EKConv: Compressing Convolutional Neural Networks with Evolutionary Kernel Convolution." Journal of Physics: Conference Series 2425, no. 1 (2023): 012011. http://dx.doi.org/10.1088/1742-6596/2425/1/012011.

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Abstract Convolutional neural networks (CNNs) have achieved tremendous success in visual recognition tasks but mainly rely on massive learnable parameters. To solve this problem, many effective and efficient convolution operators have been proposed, such as group-wise convolution, point-wise convolution, and depth-wise convolution. However, the above convolution operations model and optimize the weight relationship within the same convolutional layer. To reduce the network parameters, we explicitly construct the relationship between convolution kernels of adjacent layers. Specifically, we prop

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Zhang, Yulin, Feipeng Li, Haoke Xu, Xiaoming Li, and Shan Jiang. "Efficient Convolutional Neural Networks Utilizing Fine-Grained Fast Fourier Transforms." Electronics 13, no. 18 (2024): 3765. http://dx.doi.org/10.3390/electronics13183765.

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Convolutional Neural Networks (CNNs) are among the most prevalent deep learning techniques employed across various domains. The computational complexity of CNNs is largely attributed to the convolution operations. These operations are computationally demanding and significantly impact overall model performance. Traditional CNN implementations convert convolutions into matrix operations via the im2col (image to column) technique, facilitating parallelization through advanced BLAS libraries. This study identifies and investigates a significant yet intricate pattern of data redundancy within the

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Geum, Young Hee, Arjun Kumar Rathie, and Hwajoon Kim. "Matrix Expression of Convolution and Its Generalized Continuous Form." Symmetry 12, no. 11 (2020): 1791. http://dx.doi.org/10.3390/sym12111791.

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In this paper, we consider the matrix expression of convolution, and its generalized continuous form. The matrix expression of convolution is effectively applied in convolutional neural networks, and in this study, we correlate the concept of convolution in mathematics to that in convolutional neural network. Of course, convolution is a main process of deep learning, the learning method of deep neural networks, as a core technology. In addition to this, the generalized continuous form of convolution has been expressed as a new variant of Laplace-type transform that, encompasses almost all exis

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Feng, Yifan, Haoxuan You, Zizhao Zhang, Rongrong Ji, and Yue Gao. "Hypergraph Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3558–65. http://dx.doi.org/10.1609/aaai.v33i01.33013558.

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In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure. Confronting the challenges of learning representation for complex data in real practice, we propose to incorporate such data structure in a hypergraph, which is more flexible on data modeling, especially when dealing with complex data. In this method, a hyperedge convolution operation is designed to handle the data correlation during representation learning. In this way, traditional hypergraph learning procedure can be

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D P, Mrs Manju Bhargavi, Charmala Rajesh Karna, harsha A S, and harsha H. "Estimating Indian Food Calorie With Convolution Neural Networks." International Journal of Research Publication and Reviews 6, no. 5 (2025): 12846–50. https://doi.org/10.55248/gengpi.6.0525.1908.

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Liu, Zhizhe, Luo Sun, and Qian Zhang. "High Similarity Image Recognition and Classification Algorithm Based on Convolutional Neural Network." Computational Intelligence and Neuroscience 2022 (April 12, 2022): 1–10. http://dx.doi.org/10.1155/2022/2836486.

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Nowadays, the information processing capabilities and resource storage capabilities of computers have been greatly improved, which also provides support for the neural network technology. Convolutional neural networks have good characterization capabilities in computer vision tasks, such as image recognition technology. Aiming at the problem of high similarity image recognition and classification in a specific field, this paper proposes a high similarity image recognition and classification algorithm fused with convolutional neural networks. First, we extract the image texture features, train

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9

Syamala Rao, P., Dr G.P.SaradhiVarma, and Rajasekhar Mutukuri. "Effective and High Computing Algorithms for Convolution Neural Networks." International Journal of Engineering & Technology 7, no. 3.31 (2018): 66. http://dx.doi.org/10.14419/ijet.v7i3.31.18203.

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Training a large set of data takes GPU days using Deep convolution neural networks which are a time taking process. Self-driving cars require very low latency for pedestrian detection. Image recognition constrained by limited processing resources for mobile phones. The computation speed of the training set determines that in these situations convolution neural networks was a success. For large filters, Conventional Faster Fourier Transform based convolution is preferably fast, yet in case of small, 3 × 3 filters state of the art convolutional neural networks is used. By using Winograd's minima

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Xianhao Shen, Xianhao Shen, Changhong Zhu Xianhao Shen, Yihao Zang Changhong Zhu, and Shaohua Niu Yihao Zang. "A Method for Detecting Abnormal Data of Network Nodes Based on Convolutional Neural Network." 電腦學刊 33, no. 3 (2022): 049–58. http://dx.doi.org/10.53106/199115992022063303004.

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<p>Abnormal data detection is an important step to ensure the accuracy and reliability of node data in wireless sensor networks. In this paper, a data classification method based on convolutional neural network is proposed to solve the problem of data anomaly detection in wireless sensor networks. First, Normal data and abnormal data generated after injection fault are normalized and mapped to gray image as input data of the convolutional neural network. Then, based on the classical convolution neural network, three new convolutional neural network models are designed by designing the pa

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Sarada, N., and K. Thirupathi Rao. "A Neural Network Architecture Using Separable Neural Networks for the Identification of “Pneumonia” in Digital Chest Radiographs." International Journal of e-Collaboration 17, no. 1 (2021): 89–100. http://dx.doi.org/10.4018/ijec.2021010106.

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In recent years, convolutional neural networks had a wide impact in the fields of medical image processing. Image semantic segmentation and image classification have been the main challenges in this field. These two techniques have been seeing a lot of improvement in medical surgeries which are being carried out by robots and autonomous machines. This work will be working on a convolutional model to detect pneumonia in a given chest x-ray scan. In addition to the convolution model, the proposed model consists of deep separable convolution kernels which replace few convolutional layers; one mai

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Gafarov, Fail, Andrey Berdnikov, and Pavel Ustin. "Online social network user performance prediction by graph neural networks." International Journal of Advances in Intelligent Informatics 8, no. 3 (2022): 285. http://dx.doi.org/10.26555/ijain.v8i3.859.

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Online social networks provide rich information that characterizes the user’s personality, his interests, hobbies, and reflects his current state. Users of social networks publish photos, posts, videos, audio, etc. every day. Online social networks (OSN) open up a wide range of research opportunities for scientists. Much research conducted in recent years using graph neural networks (GNN) has shown their advantages over conventional deep learning. In particular, the use of graph neural networks for online social network analysis seems to be the most suitable. In this article we studied the use

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Cheng, Chao, and Keshab K. Parhi. "Fast 2D Convolution Algorithms for Convolutional Neural Networks." IEEE Transactions on Circuits and Systems I: Regular Papers 67, no. 5 (2020): 1678–91. http://dx.doi.org/10.1109/tcsi.2020.2964748.

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Ghiasi-Shirazi, Kamaledin. "Generalizing the Convolution Operator in Convolutional Neural Networks." Neural Processing Letters 50, no. 3 (2019): 2627–46. http://dx.doi.org/10.1007/s11063-019-10043-7.

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Chegeni, Milad Kohzadi, Abdolreza Rashno, and Sadegh Fadaei. "Convolution-layer parameters optimization in Convolutional Neural Networks." Knowledge-Based Systems 261 (February 2023): 110210. http://dx.doi.org/10.1016/j.knosys.2022.110210.

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Peng, Wenli, Shenglai Zhen, Xin Chen, Qianjing Xiong, and Benli Yu. "Study on convolutional recurrent neural networks for speech enhancement in fiber-optic microphones." Journal of Physics: Conference Series 2246, no. 1 (2022): 012084. http://dx.doi.org/10.1088/1742-6596/2246/1/012084.

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Abstract In this paper, several improved convolutional recurrent networks (CRN) are proposed, which can enhance the speech with non-additive distortion captured by fiber-optic microphones. Our preliminary study shows that the original CRN structure based on amplitude spectrum estimation is seriously distorted due to the loss of phase information. Therefore, we transform the network to run in time domain and gain 0.42 improvement on PESQ and 0.03 improvement on STOI. In addition, we integrate dilated convolution into CRN architecture, and adopt three different types of bottleneck modules, namel

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Aizenberg, Igor, and Alexander Vasko. "Frequency-Domain and Spatial-Domain MLMVN-Based Convolutional Neural Networks." Algorithms 17, no. 8 (2024): 361. http://dx.doi.org/10.3390/a17080361.

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This paper presents a detailed analysis of a convolutional neural network based on multi-valued neurons (CNNMVN) and a fully connected multilayer neural network based on multi-valued neurons (MLMVN), employed here as a convolutional neural network in the frequency domain. We begin by providing an overview of the fundamental concepts underlying CNNMVN, focusing on the organization of convolutional layers and the CNNMVN learning algorithm. The error backpropagation rule for this network is justified and presented in detail. Subsequently, we consider how MLMVN can be used as a convolutional neura

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Wan, Renzhuo, Shuping Mei, Jun Wang, Min Liu, and Fan Yang. "Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting." Electronics 8, no. 8 (2019): 876. http://dx.doi.org/10.3390/electronics8080876.

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Multivariable time series prediction has been widely studied in power energy, aerology, meteorology, finance, transportation, etc. Traditional modeling methods have complex patterns and are inefficient to capture long-term multivariate dependencies of data for desired forecasting accuracy. To address such concerns, various deep learning models based on Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) methods are proposed. To improve the prediction accuracy and minimize the multivariate time series data dependence for aperiodic data, in this article, Beijing PM2.5 and ISO-N

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Varghese, Prathibha, and Arockia Selva Saroja. "Biologically inspired deep residual networks." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 4 (2023): 1873. http://dx.doi.org/10.11591/ijai.v12.i4.pp1873-1882.

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<p>Many difficult computer vision issues have been effectively tackled by deep neural networks. Not only that but it was discovered that traditional residual neural networks (ResNet) captures features with high generalizability, rendering it a cutting-edge convolutional neural network (CNN). The images classified by the authors of this research introduce a deep residual neural network that is biologically inspired introduces hexagonal convolutions along the skip connection. With the competitive training techniques, the effectiveness of several ResNet variations using square and hexagonal

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Varghese, Prathibha, and Arockia Selva Saroja. "Biologically inspired deep residual networks." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 4 (2023): 1873–82. https://doi.org/10.11591/ijai.v12.i4.pp1873-1882.

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Many difficult computer vision issues have been effectively tackled by deep neural networks. Not only that but it was discovered that traditional residual neural networks (ResNet) captures features with high generalizability, rendering it a cutting-edge convolutional neural network (CNN). The images classified by the authors of this research introduce a deep residual neural network that is biologically inspired introduces hexagonal convolutions along the skip connection. With the competitive training techniques, the effectiveness of several ResNet variations using square and hexagona

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21

Shi, Lin, and Lei Zheng. "An IGWOCNN Deep Method for Medical Education Quality Estimating." Mathematical Problems in Engineering 2022 (August 9, 2022): 1–5. http://dx.doi.org/10.1155/2022/9037726.

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The deep learning and mining ability of big data are used to analyze the shortcomings in the teaching scheme, and the teaching scheme is optimized to improve the teaching ability. The convolution neural network optimized by improved grey wolf optimization is used to train the data so as to improve the efficiency of searching the optimal value of the algorithm and prevent the algorithm from tending to the local optimal value. In order to solve the shortcoming of grey wolf optimization, an improved grey wolf optimization, that is, grey wolf optimization with variable convergence factor, is used

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22

Kubanek, Mariusz, Janusz Bobulski, and Joanna Kulawik. "A Method of Speech Coding for Speech Recognition Using a Convolutional Neural Network." Symmetry 11, no. 9 (2019): 1185. http://dx.doi.org/10.3390/sym11091185.

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This work presents a new approach to speech recognition, based on the specific coding of time and frequency characteristics of speech. The research proposed the use of convolutional neural networks because, as we know, they show high resistance to cross-spectral distortions and differences in the length of the vocal tract. Until now, two layers of time convolution and frequency convolution were used. A novel idea is to weave three separate convolution layers: traditional time convolution and the introduction of two different frequency convolutions (mel-frequency cepstral coefficients (MFCC) co

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AL-Janabi, Amna Manaf, and Hawraa Abbas Almurieb. "Convolutional Neural Networks Approximation in Quasi-Orlicz Spaces on Sphers." Journal of Kufa for Mathematics and Computer 11, no. 1 (2024): 1–5. http://dx.doi.org/10.31642/jokmc/2018/110101.

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It is necessary to study the theoretical bases of an approximation deep convolutional neural networks, because of its interesting developments in vital domains. The approximation abilities of deep-convolution neural networks produced by downsampling operators in quasi- Orlicz spaces have been studied, since this space is wider and more important than other spaces. In this paper, we define quasi-Orlicz norm on spherical spaces. In addition, modulus of smoothness is also studied in terms of quasi-Orlicz norm. Finally, Function approximation theorems are studied by using convolution neural networ

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Kurshid, Madina, and Mansotra Saksham. "Genderpredictions using Convolution Neural Networks." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 3 (2020): 537–40. https://doi.org/10.35940/ijrte.C4606.099320.

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Nowadays Deep learning was advanced so much in our daily life. From 2014, there is massive growth in this technology as there is a vast amount of data present. We are even getting better results from whatever we may do. In my work, I have used Convolution Neural Networks as my project depends on image classification. So what I’m trying to do is I’m using two classes in which one class is male and one class is female. I’m classifying both the classes and trying to predict who is male and who is female. For this, I have been using layers like Sequential, Convolution2D, Max-pool

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Akbar, Mutaqin. "Traffic sign recognition using convolutional neural networks." Jurnal Teknologi dan Sistem Komputer 9, no. 2 (2021): 120–25. http://dx.doi.org/10.14710/jtsiskom.2021.13959.

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Traffic sign recognition (TSR) can be used to recognize traffic signs by utilizing image processing. This paper presents traffic sign recognition in Indonesia using convolutional neural networks (CNN). The overall image dataset used is 2050 images of traffic signs, consisting of 10 kinds of signs. The CNN layer used in this study consists of one convolution layer, one pooling layer using maxpool operation, and one fully connected layer. The training algorithm used is stochastic gradient descent (SGD). At the training stage, using 1750 training images, 48 filters, and a learning rate of 0.005,

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Li, Zhenxin, Yong Han, Zhenyu Xu, Zhihao Zhang, Zhixian Sun, and Ge Chen. "PMGCN: Progressive Multi-Graph Convolutional Network for Traffic Forecasting." ISPRS International Journal of Geo-Information 12, no. 6 (2023): 241. http://dx.doi.org/10.3390/ijgi12060241.

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Traffic forecasting has always been an important part of intelligent transportation systems. At present, spatiotemporal graph neural networks are widely used to capture spatiotemporal dependencies. However, most spatiotemporal graph neural networks use a single predefined matrix or a single self-generated matrix. It is difficult to obtain deeper spatial information by only relying on a single adjacency matrix. In this paper, we present a progressive multi-graph convolutional network (PMGCN), which includes spatiotemporal attention, multi-graph convolution, and multi-scale convolution modules.

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Zhang, Xin, Yingze Song, Tingting Song, et al. "LDConv: Linear deformable convolution for improving convolutional neural networks." Image and Vision Computing 149 (September 2024): 105190. http://dx.doi.org/10.1016/j.imavis.2024.105190.

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Salman, Hasan Ahmed, and Ali Kalakech. "Image Enhancement using Convolution Neural Networks." Babylonian Journal of Machine Learning 2024 (January 25, 2024): 30–47. http://dx.doi.org/10.58496/bjml/2024/003.

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The research presents a comprehensive exploration of the topic of image enhancement using convolutional neural networks (CNN).The research goes deeper into the advanced field of image processing based on the use of neural networks to automatically and efficiently improve the quality and detail of images. The thesis shows that convolutional neural networks are one of the types of deep neural networks, which are specially designed to gain knowledge from big data and extract complex features and patterns found in images. The different layers of the grid are discussed in detail, dealing with image

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Sineglazov, Victor, and Petro Chynnyk. "Quantum Convolution Neural Network." Electronics and Control Systems 2, no. 76 (2023): 40–45. http://dx.doi.org/10.18372/1990-5548.76.17667.

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In this work, quantum convolutional neural networks are considered in the task of recognizing handwritten digits. A proprietary quantum scheme for the convolutional layer of a quantum convolutional neural network is proposed. A proprietary quantum scheme for the pooling layer of a quantum convolutional neural network is proposed. The results of learning quantum convolutional neural networks are analyzed. The built models were compared and the best one was selected based on the accuracy, recall, precision and f1-score metrics. A comparative analysis was made with the classic convolutional neura

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Liu, Yang, Yiheng Zhang, Xiaoran Hao, et al. "Design of a Convolutional Neural Network Accelerator Based on On-Chip Data Reordering." Electronics 13, no. 5 (2024): 975. http://dx.doi.org/10.3390/electronics13050975.

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Convolutional neural networks have been widely applied in the field of computer vision. In convolutional neural networks, convolution operations account for more than 90% of the total computational workload. The current mainstream approach to achieving high energy-efficient convolution operations is through dedicated hardware accelerators. Convolution operations involve a significant amount of weights and input feature data. Due to limited on-chip cache space in accelerators, there is a significant amount of off-chip DRAM memory access involved in the computation process. The latency of DRAM a

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Tian, Feng, Shiao Zhang, Miao Cao, and Xiaojun Huang. "Research on accelerated coding absorber design with deep learning." Physica Scripta 98, no. 9 (2023): 096003. http://dx.doi.org/10.1088/1402-4896/acf00a.

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Abstract The traditional design of metamaterials requires a large amount of prior knowledge in electromagnetism and is time-consuming and labour-intensive, but these challenges can be addressed by using trained neural networks to accelerate the forward design process. However, when it comes to coded absorbers, there is no clear ‘guidance manual’ on which neural network is most effective for this task. In this paper, three basic neural networks (full connection, one-dimensional convolution and two-dimensional convolution) are designed considering the apparent pattern and structural parameters o

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Cho, Hyungmin. "RiSA: A Reinforced Systolic Array for Depthwise Convolutions and Embedded Tensor Reshaping." ACM Transactions on Embedded Computing Systems 20, no. 5s (2021): 1–20. http://dx.doi.org/10.1145/3476984.

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Depthwise convolutions are widely used in convolutional neural networks (CNNs) targeting mobile and embedded systems. Depthwise convolution layers reduce the computation loads and the number of parameters compared to the conventional convolution layers. Many deep neural network (DNN) accelerators adopt an architecture that exploits the high data-reuse factor of DNN computations, such as a systolic array. However, depthwise convolutions have low data-reuse factor and under-utilize the processing elements (PEs) in systolic arrays. In this paper, we present a DNN accelerator design called RiSA, w

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Fan, Yuchen, Jiahui Yu, Ding Liu, and Thomas S. Huang. "Scale-Wise Convolution for Image Restoration." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 10770–77. http://dx.doi.org/10.1609/aaai.v34i07.6706.

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While scale-invariant modeling has substantially boosted the performance of visual recognition tasks, it remains largely under-explored in deep networks based image restoration. Naively applying those scale-invariant techniques (e.g., multi-scale testing, random-scale data augmentation) to image restoration tasks usually leads to inferior performance. In this paper, we show that properly modeling scale-invariance into neural networks can bring significant benefits to image restoration performance. Inspired from spatial-wise convolution for shift-invariance, “scale-wise convolution” is proposed

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Wang, Lei. "Application Research of Deep Convolutional Neural Network in Computer Vision." Journal of Networking and Telecommunications 2, no. 2 (2020): 23. http://dx.doi.org/10.18282/jnt.v2i2.886.

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<p>As an important research achievement in the field of brain like computing, deep convolution neural network has been widely used in many fields such as computer vision, natural language processing, information retrieval, speech recognition, semantic understanding and so on. It has set off a wave of neural network research in industry and academia and promoted the development of artificial intelligence. At present, the deep convolution neural network mainly simulates the complex hierarchical cognitive laws of the human brain by increasing the number of layers of the network, using a lar

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Belorutsky, R. Yu, and S. V. Zhitnik. "SPEECH RECOGNITION BASED ON CONVOLUTION NEURAL NETWORKS." Issues of radio electronics, no. 4 (May 10, 2019): 47–52. http://dx.doi.org/10.21778/2218-5453-2019-4-47-52.

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The problem of recognizing a human speech in the form of digits from one to ten recorded by dictaphone is considered. The method of the sound signal spectrogram recognition by means of convolutional neural networks is used. The algorithms for input data preliminary processing, networks training and words recognition are realized. The recognition accuracy for different number of convolution layers is estimated. Its number is determined and the structure of neural network is proposed. The comparison of recognition accuracy when the input data for the network is spectrogram or first two formants

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Diah Putri Kartikasari, Fiqri Dian Priyatna Sinaga, Tiara Ayu Triarta Tambak, Zahra Humaira Kudadiri, and M. Khalil Gibran. "Ekstraksi Fitur Citra Grayscale dengan Convolutional Neural Networks." Jurnal Teknik Informatika dan Teknologi Informasi 5, no. 1 (2025): 198–205. https://doi.org/10.55606/jutiti.v5i1.5175.

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This study aims to explore the use of Convolutional Neural Networks (CNN) in feature extraction from grayscale images for avocado object identification. The process begins with taking a grayscale image of the avocado object to be recognized. Convolution is applied using a 3x3 horizontal Sobel kernel filter with a stride of 1 to the right, and a ReLU (Rectified Linear Unit) activation function to improve the network's ability to extract relevant features. After the convolution stage, pooling is carried out using the max pooling method to reduce the image dimension while retaining important info

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Chimakurthi, Venkata Naga Satya Surendra. "Application of Convolution Neural Network for Digital Image Processing." Engineering International 8, no. 2 (2020): 149—xxx. http://dx.doi.org/10.18034/ei.v8i2.592.

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In order to train neural network algorithms for multiple machine learning tasks, like the division of distinct categories of objects, various deep learning approaches employ data. Convolutional neural networks deep learning algorithms are quite strong when it comes to image processing. With the recent development of multi-layer convolutional neural networks for high-level tasks like object recognition, object acquisition, and recent semantic classification, the field has seen great success in this approach. The two-phase approach is frequently employed in semantic segregation. In the second st

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Oh, Seokjin, Jiyong An, and Kyeong-Sik Min. "Area-Efficient Mapping of Convolutional Neural Networks to Memristor Crossbars Using Sub-Image Partitioning." Micromachines 14, no. 2 (2023): 309. http://dx.doi.org/10.3390/mi14020309.

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Memristor crossbars can be very useful for realizing edge-intelligence hardware, because the neural networks implemented by memristor crossbars can save significantly more computing energy and layout area than the conventional CMOS (complementary metal–oxide–semiconductor) digital circuits. One of the important operations used in neural networks is convolution. For performing the convolution by memristor crossbars, the full image should be partitioned into several sub-images. By doing so, each sub-image convolution can be mapped to small-size unit crossbars, of which the size should be defined

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V, Roseline, and Heren Chellam G. "A Novel Fusion Attention Algorithm for Sentimental Image Analysis." Indian Journal of Science and Technology 15, no. 9 (2022): 386–94. https://doi.org/10.17485/IJST/v15i9.2159.

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Abstract <strong>Objectives:</strong> To implement a novel and hybrid methodology for finding out the positive features when using convolutional neural networks (CNNs) for visual sentiment analysis. To achieve increased accuracy, precision and recall by using this proposed fusion attention methodology. <strong>Methods:</strong> This study proposes a modified methodology encompassing spatial attention, channel attention as well as squeeze excitation modules. An enhanced approach on the basis of convolutional neural networks was used here which utilizes convolution operators by co

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Yang, Liming, Yihang Yang, Jinghui Yang, et al. "FusionNet: A Convolution–Transformer Fusion Network for Hyperspectral Image Classification." Remote Sensing 14, no. 16 (2022): 4066. http://dx.doi.org/10.3390/rs14164066.

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In recent years, deep-learning-based hyperspectral image (HSI) classification networks have become one of the most dominant implementations in HSI classification tasks. Among these networks, convolutional neural networks (CNNs) and attention-based networks have prevailed over other HSI classification networks. While convolutional neural networks with perceptual fields can effectively extract local features in the spatial dimension of HSI, they are poor at capturing the global and sequential features of spectral–spatial information; networks based on attention mechanisms, for example, Transform

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Dan, Yongping, Zhida Wang, Hengyi Li, and Jintong Wei. "Sa-SNN: spiking attention neural network for image classification." PeerJ Computer Science 10 (November 25, 2024): e2549. http://dx.doi.org/10.7717/peerj-cs.2549.

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Spiking neural networks (SNNs) are known as third generation neural networks due to their energy efficient and low power consumption. SNNs have received a lot of attention due to their biological plausibility. SNNs are closer to the way biological neural systems work by simulating the transmission of information through discrete spiking signals between neurons. Influenced by the great potential shown by the attention mechanism in convolutional neural networks, Therefore, we propose a Spiking Attention Neural Network (Sa-SNN). The network includes a novel Spiking-Efficient Channel Attention (SE

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Doughan, Ziad, Rola Kassem, Ahmad M. El-Hajj, and Ali M. Haidar. "Novel Preprocessors for Convolution Neural Networks." IEEE Access 10 (2022): 36834–45. http://dx.doi.org/10.1109/access.2022.3163405.

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Nie, Chang, and Huan Wang. "Tensor neural networks via circulant convolution." Neurocomputing 483 (April 2022): 22–31. http://dx.doi.org/10.1016/j.neucom.2022.01.010.

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Gu, Manchang. "Convolution Neural Networks Embedding K-means." Journal of Information and Computational Science 12, no. 17 (2015): 6391–400. http://dx.doi.org/10.12733/jics20107075.

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Yadla, Prasanth. "Plant Phenotyping Using Convolution Neural Networks." International Journal of Science, Engineering and Technology 12, no. 6 (2024): 1–7. https://doi.org/10.61463/ijset.vol.12.issue6.371.

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Srinivas, K., B. Kavitha Rani, M. Varaprasad Rao, G. Madhukar, and B. Venkata Ramana. "Convolution Neural Networks for Binary Classification." Journal of Computational and Theoretical Nanoscience 16, no. 11 (2019): 4877–82. http://dx.doi.org/10.1166/jctn.2019.8399.

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Convolutional neural networks (CNNs) are similar to “ordinary” neural networks in the sense that they are made up of hidden layers consisting of neurons with “learnable” parameters. These neurons receive inputs, perform a dot product, and then follows it with a non-linearity. The whole network expresses the mapping between raw image pixels and their class scores. Conventionally, the Softmax function is the classifier used at the last layer of this network. However, there have been studies conducted to challenge this norm. Empirical data has shown that the CNN model was able to achieve a test a

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Li, Qingyu, and Zhenjiang Miao. "Self-attentional Convolution for Neural Networks." Journal of Physics: Conference Series 1169 (February 2019): 012034. http://dx.doi.org/10.1088/1742-6596/1169/1/012034.

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Sonth, Dr Mahesh V., K. Sai Manidee, K. Rohith, M. Sai Akas, and K. Harichandana Priya. "Hand Safety Using Convolution Neural Networks." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (2022): 1739–44. http://dx.doi.org/10.22214/ijraset.2022.47711.

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Abstract: Detecting the hand when it crosses the safety level and in return it also raises an alert in the form of alarm. So that the threat can be identified and proper measures are taken to overcome that. The methodology of the project goes as follows, taking input from camera , Image processing to detect hand, Projecting a line using computer vision, Raising alarm when hand crosses this projected safety line. The real time data is taken from the camera as an input to the Image processing algorithm. Then this input is processed to find the hand in image in it and checks whether the hand is c

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B, Murali Krishna. "Weed Identification Using Convolution Neural Networks." International Journal of Computer Communication and Informatics 5, no. 2 (2023): 1–11. http://dx.doi.org/10.34256/ijcci2321.

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Deep learning is the core component of the machine learning field which employs knowledge representation for learning. Learning can be supervised or unsupervised. More deep learning techniques can be used which will contain deep belief, deep neural, recurrent neural networks in it which will be used in many fields. The most commonly used applications in deep learning are vision, audio, video, language processing, social media, medical, gaming and there are so many other programs where this deep learning has already produced very perfect results when compared to other cases and in a very little

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Swasthi, B. S., R. Anagha, S. Arpitha, B. S. Sanjay, and K. Harshitha. "Parking Assist using Convolution Neural Networks." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 6 (2020): 248–52. https://doi.org/10.35940/ijeat.F1379.089620.

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Parking vehicles are one of the most frustrating tasks that people face these days. Locating an available parking space is a huge headache especially in urban areas. This paper aims to design one such parking system which, in many ways reduces the hassles of parking. The paper presents a system where a Machine Learning model, Convolution Neural Network(CNN) is used to classify parking slots in a parking space into vacant and filled slots. In order to optimize the task of classification, the method of Transfer Learning is implemented in the paper. The problem of parking stands not only limited

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